------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  codevoteduc.log
  log type:  text
 opened on:  19 Apr 2016, 18:08:28

. 
. set memory 700m
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the
    fly automatically.

. set maxvar 32767


. set matsize 8000

. set more off

. 
. use mozdata.dta

. 
. set more off

. 
. global treat="civiceduc hotline verdade"

. global prov="pr1 pr2 pr3"

. 
. ****************************************************************
. *****  PRODUCING Z-SCORES AND INDICES OF SURVEY VARIABLES  *****
. ****************************************************************
. 
. *info
. 
. global info="knowelect knowdurpres knowassem2009 knowabst knowcand"

. 
. foreach i in $info {
  2. 
. sum `i' if time==0 & control==1
  3. matrix define auxm0=r(mean)
  4. matrix define auxs0=r(sd)
  5. scalar define m`i'0=auxm0[1,1]
  6. scalar define sd`i'0=auxs0[1,1]
  7. sum `i' if time==1 & control==1
  8. matrix define auxm1=r(mean)
  9. matrix define auxs1=r(sd)
 10. scalar define m`i'1=auxm1[1,1]
 11. scalar define sd`i'1=auxs1[1,1]
 12. capture drop m`i'
 13. gen m`i'=.
 14. replace m`i'=m`i'0 if time==0
 15. replace m`i'=m`i'1 if time==1
 16. capture drop sd`i'
 17. gen sd`i'=.
 18. replace sd`i'=sd`i'0 if time==0
 19. replace sd`i'=sd`i'1 if time==1
 20. matrix drop auxm0 auxm1 auxs0 auxs1
 21. 
. }

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
   knowelect |         0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
   knowelect |       260    2.319231    .8113668          1          3
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 knowdurpres |         0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 knowdurpres |       278    .8165468    .3877357          0          1
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
knowass~2009 |         0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
knowass~2009 |       277    1.476534     .694433          0          2
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    knowabst |         0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    knowabst |       267    .3857678    .7541014          0          2
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    knowcand |       451    .5720621    .4953293          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    knowcand |       278    .7122302    .4535397          0          1
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)

. 
. foreach i in $info {
  2. 
. capture drop zsc`i'
  3. 
. capture drop zzsc`i'
  4. 
. gen zsc`i'=(`i'-m`i')/(sd`i')
  5. 
. gen zzsc`i'=zsc`i'
  6. 
. sum zsc`i' if time==0 & control==1
  7. matrix define auxm=r(mean)
  8. scalar define m1`i'=auxm[1,1]
  9. replace zzsc`i'=m1`i' if zsc`i'==. & time==0 & control==1
 10. 
. sum zsc`i' if time==1 & control==1
 11. matrix define auxm=r(mean)
 12. scalar define m2`i'=auxm[1,1]
 13. replace zzsc`i'=m2`i' if zsc`i'==. & time==1 & control==1
 14. 
. sum zsc`i' if time==0 & civiceduc==1 & lazy==0
 15. matrix define auxm=r(mean)
 16. scalar define m3`i'=auxm[1,1]
 17. replace zzsc`i'=m3`i' if zsc`i'==. & time==0 & civiceduc==1 & lazy==0
 18. 
. sum zsc`i' if time==1 & civiceduc==1 & lazy==0
 19. matrix define auxm=r(mean)
 20. scalar define m4`i'=auxm[1,1]
 21. replace zzsc`i'=m4`i' if zsc`i'==. & time==1 & civiceduc==1 & lazy==0
 22. 
. sum zsc`i' if time==0 & civiceduc==1 & lazy==1
 23. matrix define auxm=r(mean)
 24. scalar define m5`i'=auxm[1,1]
 25. replace zzsc`i'=m5`i' if zsc`i'==. & time==0 & civiceduc==1 & lazy==1
 26. 
. sum zsc`i' if time==1 & civiceduc==1 & lazy==1
 27. matrix define auxm=r(mean)
 28. scalar define m6`i'=auxm[1,1]
 29. replace zzsc`i'=m6`i' if zsc`i'==. & time==1 & civiceduc==1 & lazy==1
 30. 
. sum zsc`i' if time==0 & hotline==1 & lazy==0
 31. matrix define auxm=r(mean)
 32. scalar define m7`i'=auxm[1,1]
 33. replace zzsc`i'=m7`i' if zsc`i'==. & time==0 & hotline==1 & lazy==0
 34. 
. sum zsc`i' if time==1 & hotline==1 & lazy==0
 35. matrix define auxm=r(mean)
 36. scalar define m8`i'=auxm[1,1]
 37. replace zzsc`i'=m8`i' if zsc`i'==. & time==1 & hotline==1 & lazy==0
 38. 
. sum zsc`i' if time==0 & hotline==1 & lazy==1
 39. matrix define auxm=r(mean)
 40. scalar define m9`i'=auxm[1,1]
 41. replace zzsc`i'=m9`i' if zsc`i'==. & time==0 & hotline==1 & lazy==1
 42. 
. sum zsc`i' if time==1 & hotline==1 & lazy==1
 43. matrix define auxm=r(mean)
 44. scalar define m10`i'=auxm[1,1]
 45. replace zzsc`i'=m10`i' if zsc`i'==. & time==1 & hotline==1 & lazy==1
 46. 
. sum zsc`i' if time==0 & verdade==1 & lazy==0
 47. matrix define auxm=r(mean)
 48. scalar define m11`i'=auxm[1,1]
 49. replace zzsc`i'=m11`i' if zsc`i'==. & time==0 & verdade==1 & lazy==0
 50. 
. sum zsc`i' if time==1 & verdade==1 & lazy==0
 51. matrix define auxm=r(mean)
 52. scalar define m12`i'=auxm[1,1]
 53. replace zzsc`i'=m12`i' if zsc`i'==. & time==1 & verdade==1 & lazy==0
 54. 
. sum zsc`i' if time==0 & verdade==1 & lazy==1
 55. matrix define auxm=r(mean)
 56. scalar define m13`i'=auxm[1,1]
 57. replace zzsc`i'=m13`i' if zsc`i'==. & time==0 & verdade==1 & lazy==1
 58. 
. sum zsc`i' if time==1 & verdade==1 & lazy==1
 59. matrix define auxm=r(mean)
 60. scalar define m14`i'=auxm[1,1]
 61. replace zzsc`i'=m14`i' if zsc`i'==. & time==1 & verdade==1 & lazy==1
 62. 
. }
(2450 missing values generated)
(2450 missing values generated)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowelect |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowelect |       260   -1.43e-08           1  -1.625937   .8390401
(192 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowelect |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowelect |       232    .1484217    .9286873  -1.625937   .8390401
(136 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowelect |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowelect |        52    .1990943    .9606027  -1.625937   .8390401
(29 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowelect |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowelect |       219    .3831883    .7964771  -1.625937   .8390401
(126 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowelect |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowelect |        62    .2625536    .8847651  -1.625937   .8390401
(29 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowelect |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowelect |       202    .1190717    .9722101  -1.625937   .8390401
(147 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowelect |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowelect |        55    .2564093    .8828706  -1.625937   .8390401
(25 real changes made)
(2381 missing values generated)
(2381 missing values generated)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowdur~s |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowdur~s |       278    8.69e-08    .9999999  -2.105937     .47314
(174 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowdur~s |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowdur~s |       244    .1137605     .894984  -2.105937     .47314
(124 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowdur~s |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowdur~s |        54   -.0522274     1.04848  -2.105937     .47314
(27 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowdur~s |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowdur~s |       233    .0525181    .9548857  -2.105937     .47314
(112 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowdur~s |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowdur~s |        64    .0701593    .9438454  -2.105937     .47314
(27 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowdur~s |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowdur~s |       221    .0880291    .9212821  -2.105937     .47314
(128 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowdur~s |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknowdur~s |        57    .1111644    .9038073  -2.105937     .47314
(23 real changes made)
(2384 missing values generated)
(2384 missing values generated)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknow~2009 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknow~2009 |       277    6.82e-08           1  -2.126244   .7538031
(175 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknow~2009 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknow~2009 |       244   -.0193244    .9977976  -2.126244   .7538031
(124 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknow~2009 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknow~2009 |        54    .1404597    .9108305  -2.126244   .7538031
(27 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknow~2009 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknow~2009 |       233    .1543082    .8999802  -2.126244   .7538031
(112 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknow~2009 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknow~2009 |        62    .1963746    .8786384  -2.126244   .7538031
(29 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknow~2009 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknow~2009 |       221    .0500811    .9670784  -2.126244   .7538031
(128 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknow~2009 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscknow~2009 |        57    .0211595    1.022707  -2.126244   .7538031
(23 real changes made)
(2424 missing values generated)
(2424 missing values generated)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowabst |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowabst |       267   -1.12e-09           1  -.5115596   2.140604
(185 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowabst |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowabst |       238   -.0323872    1.007944  -.5115596   2.140604
(130 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowabst |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowabst |        53    .0889302    1.089985  -.5115596   2.140604
(28 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowabst |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowabst |       223    -.012049    1.000452  -.5115596   2.140604
(122 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowabst |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowabst |        63    .0567611    1.057499  -.5115596   2.140604
(28 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowabst |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowabst |       212    .1264608    1.099006  -.5115596   2.140604
(137 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowabst |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowabst |        52    .1259797    1.066389  -.5115596   2.140604
(28 real changes made)
(617 missing values generated)
(617 missing values generated)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowcand |       451    2.78e-08           1  -1.154913   .8639464
(1 real change made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowcand |       278    1.67e-08           1  -1.570381   .6344975
(174 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowcand |       368   -.0796508    1.008652  -1.154913   .8639464
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowcand |       244     .110387    .9404963  -1.570381   .6344975
(124 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowcand |        81     .016524    1.002552  -1.154913   .8639464
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowcand |        54    .1853555    .8963558  -1.570381   .6344975
(27 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowcand |       345    .1383275    .9701172  -1.154913   .8639464
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowcand |       233    .2181255    .8648086  -1.570381   .6344975
(112 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowcand |        91    .1540179    .9693153  -1.154913   .8639464
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowcand |        64    .3588876    .7349595  -1.570381   .6344975
(27 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowcand |       348    .0981722    .9809921  -1.154913   .8639464
(1 real change made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowcand |       221    .2354244    .8508372  -1.570381   .6344975
(128 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowcand |        80    .0564027     .995275  -1.154913   .8639464
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscknowcand |        57    .2476767     .846062  -1.570381   .6344975
(23 real changes made)

. 
. capture drop zzscinfo

. gen zzscinfo=(zzscknowelect+zzscknowdurpres+zzscknowassem2009+zzscknowabst+zzscknowcand)/5
(1766 missing values generated)

. replace zzscinfo=. if zscknowelect==. & zscknowdurpres==. & zscknowassem2009==. & zscknowabst=
> =. & zscknowcand==.
(615 real changes made, 615 to missing)

. 
. *cne
. 
. global cne="indepcne trustcne"

. 
. foreach i in $cne {
  2. 
. sum `i' if time==0 & control==1
  3. matrix define auxm0=r(mean)
  4. matrix define auxs0=r(sd)
  5. scalar define m`i'0=auxm0[1,1]
  6. scalar define sd`i'0=auxs0[1,1]
  7. sum `i' if time==1 & control==1
  8. matrix define auxm1=r(mean)
  9. matrix define auxs1=r(sd)
 10. scalar define m`i'1=auxm1[1,1]
 11. scalar define sd`i'1=auxs1[1,1]
 12. capture drop m`i'
 13. gen m`i'=.
 14. replace m`i'=m`i'0 if time==0
 15. replace m`i'=m`i'1 if time==1
 16. capture drop sd`i'
 17. gen sd`i'=.
 18. replace sd`i'=sd`i'0 if time==0
 19. replace sd`i'=sd`i'1 if time==1
 20. matrix drop auxm0 auxm1 auxs0 auxs1
 21. 
. }

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    indepcne |       369      4.0271    1.533794          1          5

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    indepcne |       258    4.034884    1.415157          1          5
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    trustcne |       387    4.315245    1.291202          1          5

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    trustcne |       262    4.221374     1.22108          1          5
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)

. 
. foreach i in $cne {
  2. 
. capture drop zsc`i'
  3. 
. capture drop zzsc`i'
  4. 
. gen zsc`i'=(`i'-m`i')/(sd`i')
  5. 
. }
(1085 missing values generated)
(974 missing values generated)

. 
. *confusion
. 
. global confusion="realbuildwho1 realbuildwho2 realbuildwho5 realbuildwho7"

. 
. foreach i in $confusion {
  2. 
. sum `i' if time==0 & control==1
  3. matrix define auxm0=r(mean)
  4. matrix define auxs0=r(sd)
  5. scalar define m`i'0=auxm0[1,1]
  6. scalar define sd`i'0=auxs0[1,1]
  7. sum `i' if time==1 & control==1
  8. matrix define auxm1=r(mean)
  9. matrix define auxs1=r(sd)
 10. scalar define m`i'1=auxm1[1,1]
 11. scalar define sd`i'1=auxs1[1,1]
 12. capture drop m`i'
 13. gen m`i'=.
 14. replace m`i'=m`i'0 if time==0
 15. replace m`i'=m`i'1 if time==1
 16. capture drop sd`i'
 17. gen sd`i'=.
 18. replace sd`i'=sd`i'0 if time==0
 19. replace sd`i'=sd`i'1 if time==1
 20. matrix drop auxm0 auxm1 auxs0 auxs1
 21. 
. }

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
realbuildw~1 |         0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
realbuildw~1 |       155    .3870968     .488665          0          1
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
realbuildw~2 |         0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
realbuildw~2 |       136    .3897059    .4894864          0          1
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
realbuildw~5 |         0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
realbuildw~5 |       140          .4     .491657          0          1
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
realbuildw~7 |         0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
realbuildw~7 |        47    .4893617    .5052912          0          1
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)

. 
. foreach i in $confusion {
  2. 
. capture drop zsc`i'
  3. 
. capture drop zzsc`i'
  4. 
. gen zsc`i'=(`i'-m`i')/(sd`i')
  5. 
. gen zzsc`i'=zsc`i'
  6. 
. sum zsc`i' if time==0 & control==1
  7. matrix define auxm=r(mean)
  8. scalar define m1`i'=auxm[1,1]
  9. replace zzsc`i'=m1`i' if zsc`i'==. & time==0 & control==1
 10. 
. sum zsc`i' if time==1 & control==1
 11. matrix define auxm=r(mean)
 12. scalar define m2`i'=auxm[1,1]
 13. replace zzsc`i'=m2`i' if zsc`i'==. & time==1 & control==1
 14. 
. sum zsc`i' if time==0 & civiceduc==1 & lazy==0
 15. matrix define auxm=r(mean)
 16. scalar define m3`i'=auxm[1,1]
 17. replace zzsc`i'=m3`i' if zsc`i'==. & time==0 & civiceduc==1 & lazy==0
 18. 
. sum zsc`i' if time==1 & civiceduc==1 & lazy==0
 19. matrix define auxm=r(mean)
 20. scalar define m4`i'=auxm[1,1]
 21. replace zzsc`i'=m4`i' if zsc`i'==. & time==1 & civiceduc==1 & lazy==0
 22. 
. sum zsc`i' if time==0 & civiceduc==1 & lazy==1
 23. matrix define auxm=r(mean)
 24. scalar define m5`i'=auxm[1,1]
 25. replace zzsc`i'=m5`i' if zsc`i'==. & time==0 & civiceduc==1 & lazy==1
 26. 
. sum zsc`i' if time==1 & civiceduc==1 & lazy==1
 27. matrix define auxm=r(mean)
 28. scalar define m6`i'=auxm[1,1]
 29. replace zzsc`i'=m6`i' if zsc`i'==. & time==1 & civiceduc==1 & lazy==1
 30. 
. sum zsc`i' if time==0 & hotline==1 & lazy==0
 31. matrix define auxm=r(mean)
 32. scalar define m7`i'=auxm[1,1]
 33. replace zzsc`i'=m7`i' if zsc`i'==. & time==0 & hotline==1 & lazy==0
 34. 
. sum zsc`i' if time==1 & hotline==1 & lazy==0
 35. matrix define auxm=r(mean)
 36. scalar define m8`i'=auxm[1,1]
 37. replace zzsc`i'=m8`i' if zsc`i'==. & time==1 & hotline==1 & lazy==0
 38. 
. sum zsc`i' if time==0 & hotline==1 & lazy==1
 39. matrix define auxm=r(mean)
 40. scalar define m9`i'=auxm[1,1]
 41. replace zzsc`i'=m9`i' if zsc`i'==. & time==0 & hotline==1 & lazy==1
 42. 
. sum zsc`i' if time==1 & hotline==1 & lazy==1
 43. matrix define auxm=r(mean)
 44. scalar define m10`i'=auxm[1,1]
 45. replace zzsc`i'=m10`i' if zsc`i'==. & time==1 & hotline==1 & lazy==1
 46. 
. sum zsc`i' if time==0 & verdade==1 & lazy==0
 47. matrix define auxm=r(mean)
 48. scalar define m11`i'=auxm[1,1]
 49. replace zzsc`i'=m11`i' if zsc`i'==. & time==0 & verdade==1 & lazy==0
 50. 
. sum zsc`i' if time==1 & verdade==1 & lazy==0
 51. matrix define auxm=r(mean)
 52. scalar define m12`i'=auxm[1,1]
 53. replace zzsc`i'=m12`i' if zsc`i'==. & time==1 & verdade==1 & lazy==0
 54. 
. sum zsc`i' if time==0 & verdade==1 & lazy==1
 55. matrix define auxm=r(mean)
 56. scalar define m13`i'=auxm[1,1]
 57. replace zzsc`i'=m13`i' if zsc`i'==. & time==0 & verdade==1 & lazy==1
 58. 
. sum zsc`i' if time==1 & verdade==1 & lazy==1
 59. matrix define auxm=r(mean)
 60. scalar define m14`i'=auxm[1,1]
 61. replace zzsc`i'=m14`i' if zsc`i'==. & time==1 & verdade==1 & lazy==1
 62. 
. }
(2887 missing values generated)
(2887 missing values generated)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~1 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~1 |       155    2.88e-08           1  -.7921516    1.25424
(297 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~1 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~1 |       133    .1310326    1.022146  -.7921516    1.25424
(235 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~1 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~1 |        31    .0660127    1.026491  -.7921516    1.25424
(50 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~1 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~1 |       139    -.085484    .9765245  -.7921516    1.25424
(206 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~1 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~1 |        34   -.1300837    .9717457  -.7921516    1.25424
(57 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~1 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~1 |       125   -.0554506    .9862209  -.7921516    1.25424
(224 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~1 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~1 |        28   -.1343828    .9732534  -.7921516    1.25424
(52 real changes made)
(2992 missing values generated)
(2992 missing values generated)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~2 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~2 |       136   -1.93e-08           1  -.7961526   1.246805
(316 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~2 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~2 |       117    .0070615    1.002179  -.7961526   1.246805
(251 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~2 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~2 |        26    .0681756    1.029306  -.7961526   1.246805
(55 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~2 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~2 |        97   -.1643101    .9491523  -.7961526   1.246805
(248 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~2 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~2 |        31   -.2030359      .94265  -.7961526   1.246805
(60 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~2 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~2 |       108   -.0773342    .9801587  -.7961526   1.246805
(241 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~2 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~2 |        25   -.0606879    1.000841  -.7961526   1.246805
(55 real changes made)
(2979 missing values generated)
(2979 missing values generated)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~5 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~5 |       140   -3.58e-08           1  -.8135753   1.220363
(312 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~5 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~5 |       119    .0410206    1.008168  -.8135753   1.220363
(249 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~5 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~5 |        23   -.1945506    .9569109  -.8135753   1.220363
(58 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~5 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~5 |       110   -.2588649    .9099841  -.8135753   1.220363
(235 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~5 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~5 |        26     .046937    1.024762  -.8135753   1.220363
(65 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~5 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~5 |       111   -.0623008    .9860998  -.8135753   1.220363
(238 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~5 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~5 |        24   -.1355959    .9794296  -.8135753   1.220363
(56 real changes made)
(3343 missing values generated)
(3343 missing values generated)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~7 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~7 |        47    2.28e-08           1  -.9684747   1.010582
(405 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~7 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~7 |        41   -.1961598    .9773866  -.9684747   1.010582
(327 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~7 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~7 |        10   -.3747576    .9559754  -.9684747   1.010582
(71 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~7 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~7 |        32   -.2881738    .9550112  -.9684747   1.010582
(313 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~7 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~7 |        11   -.6086461     .800568  -.9684747   1.010582
(80 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~7 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~7 |        39    -.308789    .9451322  -.9684747   1.010582
(310 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~7 |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscrealbui~7 |         9   -.7485795    .6596857  -.9684747   1.010582
(71 real changes made)

. 
. capture drop zzscconfusion

. gen zzscconfusion=(zzscrealbuildwho1+zzscrealbuildwho2+zzscrealbuildwho5+zzscrealbuildwho7)/4
(1766 missing values generated)

. replace zzscconfusion=. if zscrealbuildwho1==. & zscrealbuildwho2==. & zscrealbuildwho5==. & z
> screalbuildwho7==.
(952 real changes made, 952 to missing)

. 
. *fraud
. 
. global fraud="freefair2004 freefair2009 vcount2009"

. 
. foreach i in $fraud {
  2. 
. sum `i' if time==0 & control==1
  3. matrix define auxm0=r(mean)
  4. matrix define auxs0=r(sd)
  5. scalar define m`i'0=auxm0[1,1]
  6. scalar define sd`i'0=auxs0[1,1]
  7. sum `i' if time==1 & control==1
  8. matrix define auxm1=r(mean)
  9. matrix define auxs1=r(sd)
 10. scalar define m`i'1=auxm1[1,1]
 11. scalar define sd`i'1=auxs1[1,1]
 12. capture drop m`i'
 13. gen m`i'=.
 14. replace m`i'=m`i'0 if time==0
 15. replace m`i'=m`i'1 if time==1
 16. capture drop sd`i'
 17. gen sd`i'=.
 18. replace sd`i'=sd`i'0 if time==0
 19. replace sd`i'=sd`i'1 if time==1
 20. matrix drop auxm0 auxm1 auxs0 auxs1
 21. 
. }

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
freefair2004 |       374    3.532086    .7837406          1          4

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
freefair2004 |         0
(3532 missing values generated)
(1766 real changes made)
(0 real changes made)
(3532 missing values generated)
(1766 real changes made)
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
freefair2009 |       365    3.515068    .7652607          1          4

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
freefair2009 |       266    3.672932     .651689          1          4
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  vcount2009 |       350    6.151429    1.617397          1          7

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  vcount2009 |       263    6.346008    1.213023          1          7
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)

. 
. global fraudminus="freefair2004 freefair2009 vcount2009"

. 
. foreach i in $fraudminus {
  2. 
. capture drop zsc`i'
  3. 
. capture drop zzsc`i'
  4. 
. gen zsc`i'=-(`i'-m`i')/(sd`i')
  5. 
. }
(2049 missing values generated)
(990 missing values generated)
(1050 missing values generated)

. 
. *votbuying
. 
. global votbuying="vb2009diff freefair2009_3"

. 
. foreach i in $votbuying {
  2. 
. sum `i' if time==0 & control==1
  3. matrix define auxm0=r(mean)
  4. matrix define auxs0=r(sd)
  5. scalar define m`i'0=auxm0[1,1]
  6. scalar define sd`i'0=auxs0[1,1]
  7. sum `i' if time==1 & control==1
  8. matrix define auxm1=r(mean)
  9. matrix define auxs1=r(sd)
 10. scalar define m`i'1=auxm1[1,1]
 11. scalar define sd`i'1=auxs1[1,1]
 12. capture drop m`i'
 13. gen m`i'=.
 14. replace m`i'=m`i'0 if time==0
 15. replace m`i'=m`i'1 if time==1
 16. capture drop sd`i'
 17. gen sd`i'=.
 18. replace sd`i'=sd`i'0 if time==0
 19. replace sd`i'=sd`i'1 if time==1
 20. matrix drop auxm0 auxm1 auxs0 auxs1
 21. 
. }

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  vb2009diff |         0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  vb2009diff |       261    1.862069    1.001987          1          5
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
freefair20~3 |       327    3.382263    .9353604          1          4

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
freefair20~3 |       248    3.709677    .6204895          1          4
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)

. 
. global votbuyingplus="vb2009diff"

. global votbuyingminus="freefair2009_3"

. 
. foreach i in $votbuyingplus {
  2. 
. capture drop zsc`i'
  3. 
. capture drop zzsc`i'
  4. 
. gen zsc`i'=(`i'-m`i')/(sd`i')
  5. 
. gen zzsc`i'=zsc`i'
  6. 
. sum zsc`i' if time==0 & control==1
  7. matrix define auxm=r(mean)
  8. scalar define m1`i'=auxm[1,1]
  9. replace zzsc`i'=m1`i' if zsc`i'==. & time==0 & control==1
 10. 
. sum zsc`i' if time==1 & control==1
 11. matrix define auxm=r(mean)
 12. scalar define m2`i'=auxm[1,1]
 13. replace zzsc`i'=m2`i' if zsc`i'==. & time==1 & control==1
 14. 
. sum zsc`i' if time==0 & civiceduc==1 & lazy==0
 15. matrix define auxm=r(mean)
 16. scalar define m3`i'=auxm[1,1]
 17. replace zzsc`i'=m3`i' if zsc`i'==. & time==0 & civiceduc==1 & lazy==0
 18. 
. sum zsc`i' if time==1 & civiceduc==1 & lazy==0
 19. matrix define auxm=r(mean)
 20. scalar define m4`i'=auxm[1,1]
 21. replace zzsc`i'=m4`i' if zsc`i'==. & time==1 & civiceduc==1 & lazy==0
 22. 
. sum zsc`i' if time==0 & civiceduc==1 & lazy==1
 23. matrix define auxm=r(mean)
 24. scalar define m5`i'=auxm[1,1]
 25. replace zzsc`i'=m5`i' if zsc`i'==. & time==0 & civiceduc==1 & lazy==1
 26. 
. sum zsc`i' if time==1 & civiceduc==1 & lazy==1
 27. matrix define auxm=r(mean)
 28. scalar define m6`i'=auxm[1,1]
 29. replace zzsc`i'=m6`i' if zsc`i'==. & time==1 & civiceduc==1 & lazy==1
 30. 
. sum zsc`i' if time==0 & hotline==1 & lazy==0
 31. matrix define auxm=r(mean)
 32. scalar define m7`i'=auxm[1,1]
 33. replace zzsc`i'=m7`i' if zsc`i'==. & time==0 & hotline==1 & lazy==0
 34. 
. sum zsc`i' if time==1 & hotline==1 & lazy==0
 35. matrix define auxm=r(mean)
 36. scalar define m8`i'=auxm[1,1]
 37. replace zzsc`i'=m8`i' if zsc`i'==. & time==1 & hotline==1 & lazy==0
 38. 
. sum zsc`i' if time==0 & hotline==1 & lazy==1
 39. matrix define auxm=r(mean)
 40. scalar define m9`i'=auxm[1,1]
 41. replace zzsc`i'=m9`i' if zsc`i'==. & time==0 & hotline==1 & lazy==1
 42. 
. sum zsc`i' if time==1 & hotline==1 & lazy==1
 43. matrix define auxm=r(mean)
 44. scalar define m10`i'=auxm[1,1]
 45. replace zzsc`i'=m10`i' if zsc`i'==. & time==1 & hotline==1 & lazy==1
 46. 
. sum zsc`i' if time==0 & verdade==1 & lazy==0
 47. matrix define auxm=r(mean)
 48. scalar define m11`i'=auxm[1,1]
 49. replace zzsc`i'=m11`i' if zsc`i'==. & time==0 & verdade==1 & lazy==0
 50. 
. sum zsc`i' if time==1 & verdade==1 & lazy==0
 51. matrix define auxm=r(mean)
 52. scalar define m12`i'=auxm[1,1]
 53. replace zzsc`i'=m12`i' if zsc`i'==. & time==1 & verdade==1 & lazy==0
 54. 
. sum zsc`i' if time==0 & verdade==1 & lazy==1
 55. matrix define auxm=r(mean)
 56. scalar define m13`i'=auxm[1,1]
 57. replace zzsc`i'=m13`i' if zsc`i'==. & time==0 & verdade==1 & lazy==1
 58. 
. sum zsc`i' if time==1 & verdade==1 & lazy==1
 59. matrix define auxm=r(mean)
 60. scalar define m14`i'=auxm[1,1]
 61. replace zzsc`i'=m14`i' if zsc`i'==. & time==1 & verdade==1 & lazy==1
 62. 
. }
(2452 missing values generated)
(2452 missing values generated)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscvb2009d~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscvb2009d~f |       261   -4.06e-08           1  -.8603591   3.131707
(191 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscvb2009d~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscvb2009d~f |       229   -.0018034    .9925923  -.8603591   3.131707
(139 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscvb2009d~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscvb2009d~f |        53   -.2954441    .9286099  -.8603591    2.13369
(28 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscvb2009d~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscvb2009d~f |       219   -.0218429    1.005784  -.8603591   3.131707
(126 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscvb2009d~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscvb2009d~f |        59    .1038263    1.064076  -.8603591   3.131707
(32 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscvb2009d~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscvb2009d~f |       209    .1472078    1.080896  -.8603591   3.131707
(140 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscvb2009d~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscvb2009d~f |        50    .0178954    1.000864  -.8603591   1.135674
(30 real changes made)

. 
. foreach i in $votbuyingminus {
  2. 
. capture drop zsc`i'
  3. 
. capture drop zzsc`i'
  4. 
. gen zsc`i'=-(`i'-m`i')/(sd`i')
  5. 
. gen zzsc`i'=zsc`i'
  6. 
. sum zsc`i' if time==0 & control==1
  7. matrix define auxm=r(mean)
  8. scalar define m1`i'=auxm[1,1]
  9. replace zzsc`i'=m1`i' if zsc`i'==. & time==0 & control==1
 10. 
. sum zsc`i' if time==1 & control==1
 11. matrix define auxm=r(mean)
 12. scalar define m2`i'=auxm[1,1]
 13. replace zzsc`i'=m2`i' if zsc`i'==. & time==1 & control==1
 14. 
. sum zsc`i' if time==0 & civiceduc==1 & lazy==0
 15. matrix define auxm=r(mean)
 16. scalar define m3`i'=auxm[1,1]
 17. replace zzsc`i'=m3`i' if zsc`i'==. & time==0 & civiceduc==1 & lazy==0
 18. 
. sum zsc`i' if time==1 & civiceduc==1 & lazy==0
 19. matrix define auxm=r(mean)
 20. scalar define m4`i'=auxm[1,1]
 21. replace zzsc`i'=m4`i' if zsc`i'==. & time==1 & civiceduc==1 & lazy==0
 22. 
. sum zsc`i' if time==0 & civiceduc==1 & lazy==1
 23. matrix define auxm=r(mean)
 24. scalar define m5`i'=auxm[1,1]
 25. replace zzsc`i'=m5`i' if zsc`i'==. & time==0 & civiceduc==1 & lazy==1
 26. 
. sum zsc`i' if time==1 & civiceduc==1 & lazy==1
 27. matrix define auxm=r(mean)
 28. scalar define m6`i'=auxm[1,1]
 29. replace zzsc`i'=m6`i' if zsc`i'==. & time==1 & civiceduc==1 & lazy==1
 30. 
. sum zsc`i' if time==0 & hotline==1 & lazy==0
 31. matrix define auxm=r(mean)
 32. scalar define m7`i'=auxm[1,1]
 33. replace zzsc`i'=m7`i' if zsc`i'==. & time==0 & hotline==1 & lazy==0
 34. 
. sum zsc`i' if time==1 & hotline==1 & lazy==0
 35. matrix define auxm=r(mean)
 36. scalar define m8`i'=auxm[1,1]
 37. replace zzsc`i'=m8`i' if zsc`i'==. & time==1 & hotline==1 & lazy==0
 38. 
. sum zsc`i' if time==0 & hotline==1 & lazy==1
 39. matrix define auxm=r(mean)
 40. scalar define m9`i'=auxm[1,1]
 41. replace zzsc`i'=m9`i' if zsc`i'==. & time==0 & hotline==1 & lazy==1
 42. 
. sum zsc`i' if time==1 & hotline==1 & lazy==1
 43. matrix define auxm=r(mean)
 44. scalar define m10`i'=auxm[1,1]
 45. replace zzsc`i'=m10`i' if zsc`i'==. & time==1 & hotline==1 & lazy==1
 46. 
. sum zsc`i' if time==0 & verdade==1 & lazy==0
 47. matrix define auxm=r(mean)
 48. scalar define m11`i'=auxm[1,1]
 49. replace zzsc`i'=m11`i' if zsc`i'==. & time==0 & verdade==1 & lazy==0
 50. 
. sum zsc`i' if time==1 & verdade==1 & lazy==0
 51. matrix define auxm=r(mean)
 52. scalar define m12`i'=auxm[1,1]
 53. replace zzsc`i'=m12`i' if zsc`i'==. & time==1 & verdade==1 & lazy==0
 54. 
. sum zsc`i' if time==0 & verdade==1 & lazy==1
 55. matrix define auxm=r(mean)
 56. scalar define m13`i'=auxm[1,1]
 57. replace zzsc`i'=m13`i' if zsc`i'==. & time==0 & verdade==1 & lazy==1
 58. 
. sum zsc`i' if time==1 & verdade==1 & lazy==1
 59. matrix define auxm=r(mean)
 60. scalar define m14`i'=auxm[1,1]
 61. replace zzsc`i'=m14`i' if zsc`i'==. & time==1 & verdade==1 & lazy==1
 62. 
. }
(1167 missing values generated)
(1167 missing values generated)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefai~3 |       327   -3.28e-08           1  -.6604267   2.546893
(125 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefai~3 |       248    7.02e-08           1  -.4678928      4.367
(204 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefai~3 |       275    -.096716    .9635075  -.6604267   2.546893
(93 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefai~3 |       232    -.044145    1.033315  -.4678928      4.367
(136 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefai~3 |        62    .1672687     1.24587  -.6604267   2.546893
(19 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefai~3 |        51    .0693175    1.002153  -.4678928      4.367
(30 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefai~3 |       260   -.0641942    .9901004  -.6604267   2.546893
(85 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefai~3 |       214    .0743382    1.135713  -.4678928      4.367
(131 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefai~3 |        67    .0895436    1.036453  -.6604267   2.546893
(24 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefai~3 |        58    .1156288    1.032533  -.4678928      4.367
(33 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefai~3 |       252   -.0664786    .9596185  -.6604267   2.546893
(97 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefai~3 |       206    .1892771    1.179196  -.4678928      4.367
(143 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefai~3 |        59   -.2255359    .8214038  -.6604267   2.546893
(21 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefai~3 |        54   -.1694426    .7715831  -.4678928   2.755369
(26 real changes made)

. 
. capture drop zzscvotbuying

. gen zzscvotbuying=(zzscvb2009diff+zzscfreefair2009_3)/2
(1766 missing values generated)

. replace zzscvotbuying=. if zscvb2009diff==. & zscfreefair2009_3==.
(635 real changes made, 635 to missing)

. 
. *violence
. 
. capture gen competd=competitiondiff

. 
. global violence="carefuldiff competd destroy2009diff freefair2009_4 competition intimgen2009 i
> ntim2009prewhofre"

. 
. foreach i in $violence {
  2. 
. sum `i' if time==0 & control==1
  3. matrix define auxm0=r(mean)
  4. matrix define auxs0=r(sd)
  5. scalar define m`i'0=auxm0[1,1]
  6. scalar define sd`i'0=auxs0[1,1]
  7. sum `i' if time==1 & control==1
  8. matrix define auxm1=r(mean)
  9. matrix define auxs1=r(sd)
 10. scalar define m`i'1=auxm1[1,1]
 11. scalar define sd`i'1=auxs1[1,1]
 12. capture drop m`i'
 13. gen m`i'=.
 14. replace m`i'=m`i'0 if time==0
 15. replace m`i'=m`i'1 if time==1
 16. capture drop sd`i'
 17. gen sd`i'=.
 18. replace sd`i'=sd`i'0 if time==0
 19. replace sd`i'=sd`i'1 if time==1
 20. matrix drop auxm0 auxm1 auxs0 auxs1
 21. 
. }

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 carefuldiff |         0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 carefuldiff |       258    2.891473    1.387775          1          5
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     competd |         0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     competd |       266    1.996241     1.19905          1          5
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
destroy200~f |         0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
destroy200~f |       261    1.961686    1.125947          1          5
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)
(3532 missing values generated)
(0 real changes made)
(1766 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
freefair2~_4 |       352    3.326705    .9143194          1          4

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
freefair2~_4 |       266    3.578947    .7185995          1          4
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 competition |       408     1.72549    .9210278          1          4

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 competition |       265    1.637736    .7667885          1          4
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
intimgen2009 |       434    1.076037     .409164          1          4

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
intimgen2009 |       270     1.12963     .504489          1          4
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
intim2009p~e |       434    .0184332    .1346669          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
intim2009p~e |       270     .037037    .1892033          0          1
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)
(3532 missing values generated)
(1766 real changes made)
(1766 real changes made)

. 
. global violenceplus="carefuldiff competd destroy2009diff competition intimgen2009 intim2009pre
> whofre"

. global violenceminus="freefair2009_4"

. 
. foreach i in $violenceplus {
  2. 
. capture drop zsc`i'
  3. 
. capture drop zzsc`i'
  4. 
. gen zsc`i'=(`i'-m`i')/(sd`i')
  5. 
. gen zzsc`i'=zsc`i'
  6. 
. sum zsc`i' if time==0 & control==1
  7. matrix define auxm=r(mean)
  8. scalar define m1`i'=auxm[1,1]
  9. replace zzsc`i'=m1`i' if zsc`i'==. & time==0 & control==1
 10. 
. sum zsc`i' if time==1 & control==1
 11. matrix define auxm=r(mean)
 12. scalar define m2`i'=auxm[1,1]
 13. replace zzsc`i'=m2`i' if zsc`i'==. & time==1 & control==1
 14. 
. sum zsc`i' if time==0 & civiceduc==1 & lazy==0
 15. matrix define auxm=r(mean)
 16. scalar define m3`i'=auxm[1,1]
 17. replace zzsc`i'=m3`i' if zsc`i'==. & time==0 & civiceduc==1 & lazy==0
 18. 
. sum zsc`i' if time==1 & civiceduc==1 & lazy==0
 19. matrix define auxm=r(mean)
 20. scalar define m4`i'=auxm[1,1]
 21. replace zzsc`i'=m4`i' if zsc`i'==. & time==1 & civiceduc==1 & lazy==0
 22. 
. sum zsc`i' if time==0 & civiceduc==1 & lazy==1
 23. matrix define auxm=r(mean)
 24. scalar define m5`i'=auxm[1,1]
 25. replace zzsc`i'=m5`i' if zsc`i'==. & time==0 & civiceduc==1 & lazy==1
 26. 
. sum zsc`i' if time==1 & civiceduc==1 & lazy==1
 27. matrix define auxm=r(mean)
 28. scalar define m6`i'=auxm[1,1]
 29. replace zzsc`i'=m6`i' if zsc`i'==. & time==1 & civiceduc==1 & lazy==1
 30. 
. sum zsc`i' if time==0 & hotline==1 & lazy==0
 31. matrix define auxm=r(mean)
 32. scalar define m7`i'=auxm[1,1]
 33. replace zzsc`i'=m7`i' if zsc`i'==. & time==0 & hotline==1 & lazy==0
 34. 
. sum zsc`i' if time==1 & hotline==1 & lazy==0
 35. matrix define auxm=r(mean)
 36. scalar define m8`i'=auxm[1,1]
 37. replace zzsc`i'=m8`i' if zsc`i'==. & time==1 & hotline==1 & lazy==0
 38. 
. sum zsc`i' if time==0 & hotline==1 & lazy==1
 39. matrix define auxm=r(mean)
 40. scalar define m9`i'=auxm[1,1]
 41. replace zzsc`i'=m9`i' if zsc`i'==. & time==0 & hotline==1 & lazy==1
 42. 
. sum zsc`i' if time==1 & hotline==1 & lazy==1
 43. matrix define auxm=r(mean)
 44. scalar define m10`i'=auxm[1,1]
 45. replace zzsc`i'=m10`i' if zsc`i'==. & time==1 & hotline==1 & lazy==1
 46. 
. sum zsc`i' if time==0 & verdade==1 & lazy==0
 47. matrix define auxm=r(mean)
 48. scalar define m11`i'=auxm[1,1]
 49. replace zzsc`i'=m11`i' if zsc`i'==. & time==0 & verdade==1 & lazy==0
 50. 
. sum zsc`i' if time==1 & verdade==1 & lazy==0
 51. matrix define auxm=r(mean)
 52. scalar define m12`i'=auxm[1,1]
 53. replace zzsc`i'=m12`i' if zsc`i'==. & time==1 & verdade==1 & lazy==0
 54. 
. sum zsc`i' if time==0 & verdade==1 & lazy==1
 55. matrix define auxm=r(mean)
 56. scalar define m13`i'=auxm[1,1]
 57. replace zzsc`i'=m13`i' if zsc`i'==. & time==0 & verdade==1 & lazy==1
 58. 
. sum zsc`i' if time==1 & verdade==1 & lazy==1
 59. matrix define auxm=r(mean)
 60. scalar define m14`i'=auxm[1,1]
 61. replace zzsc`i'=m14`i' if zsc`i'==. & time==1 & verdade==1 & lazy==1
 62. 
. }
(2455 missing values generated)
(2455 missing values generated)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccareful~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccareful~f |       258    3.93e-08           1  -1.362954   1.519358
(194 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccareful~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccareful~f |       227   -.1344793    .9553111  -1.362954   1.519358
(141 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccareful~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccareful~f |        53   -.0169684    .9895995  -1.362954   1.519358
(28 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccareful~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccareful~f |       217   -.1309977    .9311786  -1.362954   1.519358
(128 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccareful~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccareful~f |        60   -.3061059    .9043587  -1.362954   1.519358
(31 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccareful~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccareful~f |       210    -.062482    .9522509  -1.362954   1.519358
(139 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccareful~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccareful~f |        52   -.2959439    .9191462  -1.362954   1.519358
(28 real changes made)
(2434 missing values generated)
(2434 missing values generated)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  zsccompetd |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  zsccompetd |       266   -5.38e-09           1  -.8308581   2.505116
(186 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  zsccompetd |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  zsccompetd |       232   -.1514411    .8865474  -.8308581   2.505116
(136 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  zsccompetd |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  zsccompetd |        53   -.2801077    .8799352  -.8308581   2.505116
(28 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  zsccompetd |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  zsccompetd |       225   -.0895306    .9294681  -.8308581   2.505116
(120 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  zsccompetd |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  zsccompetd |        61    .1125115    1.060139  -.8308581   2.505116
(30 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  zsccompetd |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  zsccompetd |       208   -.0890851    .9063134  -.8308581   2.505116
(141 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  zsccompetd |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  zsccompetd |        53    .0503425     1.02685  -.8308581   2.505116
(27 real changes made)
(2432 missing values generated)
(2432 missing values generated)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscdestroy~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscdestroy~f |       261    5.19e-08           1  -.8541126   2.698452
(191 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscdestroy~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscdestroy~f |       236   -.1240306    .9634198  -.8541126   2.698452
(132 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscdestroy~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscdestroy~f |        53   -.4016634    .8816724  -.8541126   2.698452
(28 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscdestroy~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscdestroy~f |       224     -.03734    1.020732  -.8541126   2.698452
(121 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscdestroy~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscdestroy~f |        60   -.1435998    .9646296  -.8541126   2.698452
(31 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscdestroy~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscdestroy~f |       213   -.0160077      .96701  -.8541126   2.698452
(136 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscdestroy~f |         0
(0 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscdestroy~f |        53    .0005137    .8874963  -.8541126   .9221695
(27 real changes made)
(836 missing values generated)
(836 missing values generated)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccompeti~n |       408   -2.40e-08           1  -.7876964   2.469534
(44 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccompeti~n |       265    6.30e-09           1  -.8316972   3.080725
(187 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccompeti~n |       332   -.1630668    .9419229  -.7876964   2.469534
(36 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccompeti~n |       230   -.1796269    .8682398  -.8316972   3.080725
(138 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccompeti~n |        74    -.068758    1.067399  -.7876964   2.469534
(7 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccompeti~n |        53   -.3149623    .7386028  -.8316972   1.776584
(28 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccompeti~n |       323   -.0212891    .9738645  -.7876964   2.469534
(22 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccompeti~n |       222   -.0503877    .9582627  -.8316972   3.080725
(123 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccompeti~n |        88   -.1707966    .8985819  -.7876964   2.469534
(3 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccompeti~n |        61   -.1261785    1.053419  -.8316972   3.080725
(30 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccompeti~n |       306   -.0567709     .969765  -.7876964   2.469534
(43 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccompeti~n |       209   -.0641886    .9070236  -.8316972   3.080725
(140 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccompeti~n |        70   -.1207396     .892132  -.7876964   2.469534
(10 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zsccompeti~n |        55   -.0966361     .896664  -.8316972   3.080725
(25 real changes made)
(712 missing values generated)
(712 missing values generated)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscinti~2009 |       434    1.30e-07           1  -.1858345   7.146188
(18 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscinti~2009 |       270    2.25e-08           1  -.2569523   5.689659
(182 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscinti~2009 |       354    .0419967    1.177389  -.1858345   7.146188
(14 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscinti~2009 |       241    -.100679    .7835565  -.2569523   5.689659
(127 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscinti~2009 |        79   -.0311505     .985543  -.1858345   7.146188
(2 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscinti~2009 |        54   -.2202448    .2697438  -.2569523   1.725251
(27 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscinti~2009 |       333    .0049889    1.055903  -.1858345   7.146188
(12 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscinti~2009 |       231    -.076752    .8579801  -.2569523   5.689659
(114 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscinti~2009 |        89    .1162338    1.266745  -.1858345   7.146188
(2 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscinti~2009 |        62   -.1290682    .7061682  -.2569523   3.707455
(29 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscinti~2009 |       330    -.141398    .5021922  -.1858345   7.146188
(19 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscinti~2009 |       216   -.1560068    .6111778  -.2569523   3.707455
(133 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscinti~2009 |        73   -.0519163     .692518  -.1858345    4.70218
(7 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscinti~2009 |        54   -.1835374    .5394875  -.2569523   3.707455
(26 real changes made)
(721 missing values generated)
(721 missing values generated)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscintim20~e |       434    1.24e-09           1  -.1368798   7.288848
(18 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscintim20~e |       270           0           1  -.1957526   5.089568
(182 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscintim20~e |       350    .0328511    1.111349  -.1368798   7.288848
(18 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscintim20~e |       239    -.151524    .4824731  -.1957526   5.089568
(129 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscintim20~e |        79   -.0428832    .8354596  -.1368798   7.288848
(2 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscintim20~e |        54   -.0978763    .7192411  -.1957526   5.089568
(27 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscintim20~e |       332   -.0026799     .990696  -.1368798   7.288848
(13 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscintim20~e |       230    .0340439    1.080192  -.1957526   5.089568
(115 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscintim20~e |        88    .2006533    1.555635  -.1368798   7.288848
(3 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscintim20~e |        62   -.0252584    .9414586  -.1957526   5.089568
(29 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscintim20~e |       330   -.0918754    .5772129  -.1368798   7.288848
(19 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscintim20~e |       216   -.1712835    .3596205  -.1957526   5.089568
(133 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscintim20~e |        73    .0665648     1.22055  -.1368798   7.288848
(7 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscintim20~e |        54   -.0978763    .7192411  -.1957526   5.089568
(26 real changes made)

. 
. foreach i in $violenceminus {
  2. 
. capture drop zsc`i'
  3. 
. capture drop zzsc`i'
  4. 
. gen zsc`i'=-(`i'-m`i')/(sd`i')
  5. 
. gen zzsc`i'=zsc`i'
  6. 
. sum zsc`i' if time==0 & control==1
  7. matrix define auxm=r(mean)
  8. scalar define m1`i'=auxm[1,1]
  9. replace zzsc`i'=m1`i' if zsc`i'==. & time==0 & control==1
 10. 
. sum zsc`i' if time==1 & control==1
 11. matrix define auxm=r(mean)
 12. scalar define m2`i'=auxm[1,1]
 13. replace zzsc`i'=m2`i' if zsc`i'==. & time==1 & control==1
 14. 
. sum zsc`i' if time==0 & civiceduc==1 & lazy==0
 15. matrix define auxm=r(mean)
 16. scalar define m3`i'=auxm[1,1]
 17. replace zzsc`i'=m3`i' if zsc`i'==. & time==0 & civiceduc==1 & lazy==0
 18. 
. sum zsc`i' if time==1 & civiceduc==1 & lazy==0
 19. matrix define auxm=r(mean)
 20. scalar define m4`i'=auxm[1,1]
 21. replace zzsc`i'=m4`i' if zsc`i'==. & time==1 & civiceduc==1 & lazy==0
 22. 
. sum zsc`i' if time==0 & civiceduc==1 & lazy==1
 23. matrix define auxm=r(mean)
 24. scalar define m5`i'=auxm[1,1]
 25. replace zzsc`i'=m5`i' if zsc`i'==. & time==0 & civiceduc==1 & lazy==1
 26. 
. sum zsc`i' if time==1 & civiceduc==1 & lazy==1
 27. matrix define auxm=r(mean)
 28. scalar define m6`i'=auxm[1,1]
 29. replace zzsc`i'=m6`i' if zsc`i'==. & time==1 & civiceduc==1 & lazy==1
 30. 
. sum zsc`i' if time==0 & hotline==1 & lazy==0
 31. matrix define auxm=r(mean)
 32. scalar define m7`i'=auxm[1,1]
 33. replace zzsc`i'=m7`i' if zsc`i'==. & time==0 & hotline==1 & lazy==0
 34. 
. sum zsc`i' if time==1 & hotline==1 & lazy==0
 35. matrix define auxm=r(mean)
 36. scalar define m8`i'=auxm[1,1]
 37. replace zzsc`i'=m8`i' if zsc`i'==. & time==1 & hotline==1 & lazy==0
 38. 
. sum zsc`i' if time==0 & hotline==1 & lazy==1
 39. matrix define auxm=r(mean)
 40. scalar define m9`i'=auxm[1,1]
 41. replace zzsc`i'=m9`i' if zsc`i'==. & time==0 & hotline==1 & lazy==1
 42. *sum zzsc`i' if time==0 & hotline==1 & lazy==1
. 
. sum zsc`i' if time==1 & hotline==1 & lazy==1
 43. matrix define auxm=r(mean)
 44. scalar define m10`i'=auxm[1,1]
 45. replace zzsc`i'=m10`i' if zsc`i'==. & time==1 & hotline==1 & lazy==1
 46. 
. sum zsc`i' if time==0 & verdade==1 & lazy==0
 47. matrix define auxm=r(mean)
 48. scalar define m11`i'=auxm[1,1]
 49. replace zzsc`i'=m11`i' if zsc`i'==. & time==0 & verdade==1 & lazy==0
 50. 
. sum zsc`i' if time==1 & verdade==1 & lazy==0
 51. matrix define auxm=r(mean)
 52. scalar define m12`i'=auxm[1,1]
 53. replace zzsc`i'=m12`i' if zsc`i'==. & time==1 & verdade==1 & lazy==0
 54. 
. sum zsc`i' if time==0 & verdade==1 & lazy==1
 55. matrix define auxm=r(mean)
 56. scalar define m13`i'=auxm[1,1]
 57. replace zzsc`i'=m13`i' if zsc`i'==. & time==0 & verdade==1 & lazy==1
 58. 
. sum zsc`i' if time==1 & verdade==1 & lazy==1
 59. matrix define auxm=r(mean)
 60. scalar define m14`i'=auxm[1,1]
 61. replace zzsc`i'=m14`i' if zsc`i'==. & time==1 & verdade==1 & lazy==1
 62. 
. }
(1026 missing values generated)
(1026 missing values generated)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefa~_4 |       352   -5.22e-08           1  -.7363898   2.544739
(100 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefa~_4 |       266   -1.11e-07           1  -.5859351   3.588852
(186 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefa~_4 |       300   -.0728726    1.003253  -.7363898   2.544739
(68 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefa~_4 |       233   -.1439691    .8691369  -.5859351   3.588852
(135 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefa~_4 |        65     .088099    1.207899  -.7363898   2.544739
(16 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefa~_4 |        52    .0295783    .9310042  -.5859351   3.588852
(29 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefa~_4 |       283   -.0098265    1.036172  -.7363898   2.544739
(62 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefa~_4 |       219    -.064881    .9579178  -.5859351   3.588852
(126 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefa~_4 |        78    .0768815    1.063987  -.7363898   2.544739
(13 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefa~_4 |        59    .0744832    1.045085  -.5859351   3.588852
(32 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefa~_4 |       269   -.0980537    .9290241  -.7363898   2.544739
(80 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefa~_4 |       213     .021663    .9765256  -.5859351   3.588852
(136 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefa~_4 |        63   -.1114128    .8721913  -.7363898   2.544739
(17 real changes made)

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfreefa~_4 |        54   -.2509214    .8080568  -.5859351   3.588852
(26 real changes made)

. 
. capture drop zzscviolence

. gen zzscviolence=(zzsccarefuldiff+zzsccompetd+zzscdestroy2009diff+zzscfreefair2009_4+zzsccompe
> tition+zzscintimgen2009+zzscintim2009prewhofre)/7
(1766 missing values generated)

. replace zzscviolence=. if zsccarefuldiff==. & zsccompetd==. & zscdestroy2009diff==. & zscfreef
> air2009_4==. & zsccompetition==. & zscintimgen2009==. & zscintim2009prewhofre==.
(618 real changes made, 618 to missing)

. 
. ********************************
. *****  TABLES A3: BALANCE  *****
. ********************************
. 
. *******************************************
. *****  BALANCE OF EA CHARACTERISTICS  *****
. *******************************************
. 
. capture egen eadrop = mean(drops2), by(ea time)

. 
. global ea1="schoolbuild policesta electricity water sewer health recreation temple meetroom ro
> ad eadrop"

. 
. foreach i in $ea1 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & v==1
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & v==1
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & v==1
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0 & v==1
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'=r(p)
 12.         display f`i'
 13.         
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 14.         
.         xml_tab $list1, below save(balance.xml) append sheet("bal `i'")
 15.         estimates clear
 16. 
. }

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    0.00
       Model |           0     1           0           Prob > F      =  1.0000
    Residual |  1.95121951    80  .024390244           R-squared     =  0.0000
-------------+------------------------------           Adj R-squared = -0.0125
       Total |  1.95121951    81   .02408913           Root MSE      =  .15617

------------------------------------------------------------------------------
 schoolbuild |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |          0    .034493     0.00   1.000    -.0686433    .0686433
       _cons |   .9756098   .0243902    40.00   0.000     .9270716    1.024148
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.00
       Model |  7.5279e-06     1  7.5279e-06           Prob > F      =  0.9861
    Residual |  1.95060976    79  .024691263           R-squared     =  0.0000
-------------+------------------------------           Adj R-squared = -0.0127
       Total |  1.95061728    80  .024382716           Root MSE      =  .15713

------------------------------------------------------------------------------
 schoolbuild |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0006098   .0349214    -0.02   0.986    -.0701192    .0688996
       _cons |   .9756098   .0245403    39.76   0.000     .9267635    1.024456
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.00
       Model |   .00003127     1   .00003127           Prob > F      =  0.9719
    Residual |  1.94996873    78  .024999599           R-squared     =  0.0000
-------------+------------------------------           Adj R-squared = -0.0128
       Total |        1.95    79  .024683544           Root MSE      =  .15811

------------------------------------------------------------------------------
 schoolbuild |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0012508   .0353661    -0.04   0.972    -.0716593    .0691577
       _cons |   .9756098    .024693    39.51   0.000     .9264497     1.02477
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.00
       Model |  .000042631     3   .00001421           Prob > F      =  1.0000
    Residual |  3.90057849   157  .024844449           R-squared     =  0.0000
-------------+------------------------------           Adj R-squared = -0.0191
       Total |  3.90062112   160  .024378882           Root MSE      =  .15762

------------------------------------------------------------------------------
 schoolbuild |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -5.04e-17   .0348127    -0.00   1.000    -.0687617    .0687617
     hotline |  -.0006098   .0350296    -0.02   0.986    -.0697999    .0685803
     verdade |  -.0012508   .0352562    -0.04   0.972    -.0708884    .0683869
       _cons |   .9756098   .0246163    39.63   0.000     .9269879    1.024232
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.00
            Prob > F =    1.0000
.99998102


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    0.05
       Model |  .012195122     1  .012195122           Prob > F      =  0.8278
    Residual |  20.4878049    80  .256097561           R-squared     =  0.0006
-------------+------------------------------           Adj R-squared = -0.0119
       Total |        20.5    81   .25308642           Root MSE      =  .50606

------------------------------------------------------------------------------
   policesta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0243902   .1117701    -0.22   0.828    -.2468199    .1980394
       _cons |   .5121951   .0790334     6.48   0.000     .3549136    .6694766
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.01
       Model |  .003011141     1  .003011141           Prob > F      =  0.9140
    Residual |  20.2439024    79   .25625193           R-squared     =  0.0001
-------------+------------------------------           Adj R-squared = -0.0125
       Total |  20.2469136    80   .25308642           Root MSE      =  .50621

------------------------------------------------------------------------------
   policesta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0121951   .1125004    -0.11   0.914    -.2361216    .2117314
       _cons |   .5121951   .0790572     6.48   0.000     .3548356    .6695546
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.82
       Model |  .207700125     1  .207700125           Prob > F      =  0.3670
    Residual |  19.6797999    78  .252305127           R-squared     =  0.0104
-------------+------------------------------           Adj R-squared = -0.0022
       Total |     19.8875    79  .251740506           Root MSE      =   .5023

------------------------------------------------------------------------------
   policesta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.1019387   .1123528    -0.91   0.367    -.3256159    .1217385
       _cons |   .5121951   .0784461     6.53   0.000      .356021    .6683692
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.33
       Model |   .25021073     3  .083403577           Prob > F      =  0.8051
    Residual |  39.9237023   157  .254291098           R-squared     =  0.0062
-------------+------------------------------           Adj R-squared = -0.0128
       Total |   40.173913   160  .251086957           Root MSE      =  .50427

------------------------------------------------------------------------------
   policesta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0243902   .1113752    -0.22   0.827    -.2443774    .1955969
     hotline |  -.0121951   .1120692    -0.11   0.913    -.2335529    .2091627
     verdade |  -.1019387   .1127941    -0.90   0.368    -.3247284    .1208509
       _cons |   .5121951   .0787542     6.50   0.000     .3566407    .6677495
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.33
            Prob > F =    0.8051
.80512404


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    0.19
       Model |  .048780488     1  .048780488           Prob > F      =  0.6633
    Residual |  20.4390244    80  .255487805           R-squared     =  0.0024
-------------+------------------------------           Adj R-squared = -0.0101
       Total |  20.4878049    81  .252935863           Root MSE      =  .50546

------------------------------------------------------------------------------
 electricity |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0487805    .111637     0.44   0.663    -.1733842    .2709452
       _cons |   .4878049   .0789393     6.18   0.000     .3307107    .6448991
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    1.53
       Model |  .381097561     1  .381097561           Prob > F      =  0.2191
    Residual |  19.6189024    79  .248340537           R-squared     =  0.0191
-------------+------------------------------           Adj R-squared =  0.0066
       Total |          20    80         .25           Root MSE      =  .49834

------------------------------------------------------------------------------
 electricity |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .1371951   .1107502     1.24   0.219    -.0832476    .3576378
       _cons |   .4878049   .0778273     6.27   0.000     .3328935    .6427162
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.05
       Model |  .012507817     1  .012507817           Prob > F      =  0.8257
    Residual |  19.9874922    78    .2562499           R-squared     =  0.0006
-------------+------------------------------           Adj R-squared = -0.0122
       Total |          20    79  .253164557           Root MSE      =  .50621

------------------------------------------------------------------------------
 electricity |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0250156   .1132277     0.22   0.826    -.2004034    .2504346
       _cons |   .4878049   .0790569     6.17   0.000     .3304146    .6451951
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.57
       Model |  .429963506     3  .143321169           Prob > F      =  0.6363
    Residual |  39.5576141   157  .251959326           R-squared     =  0.0108
-------------+------------------------------           Adj R-squared = -0.0082
       Total |  39.9875776   160   .24992236           Root MSE      =  .50196

------------------------------------------------------------------------------
 electricity |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0487805   .1108634     0.44   0.661    -.1701957    .2677567
     hotline |   .1371951   .1115542     1.23   0.221    -.0831455    .3575357
     verdade |   .0250156   .1122758     0.22   0.824    -.1967502    .2467815
       _cons |   .4878049   .0783923     6.22   0.000     .3329653    .6426445
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.57
            Prob > F =    0.6363
.63634291


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    0.00
       Model |  2.8422e-14     1  2.8422e-14           Prob > F      =  1.0000
    Residual |  17.7560976    80   .22195122           R-squared     =  0.0000
-------------+------------------------------           Adj R-squared = -0.0125
       Total |  17.7560976    81  .219211081           Root MSE      =  .47112

------------------------------------------------------------------------------
       water |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -2.17e-17   .1040523    -0.00   1.000    -.2070707    .2070707
       _cons |   .3170732   .0735761     4.31   0.000      .170652    .4634943
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    1.43
       Model |  .277506775     1  .277506775           Prob > F      =  0.2345
    Residual |  15.2780488    79  .193393023           R-squared     =  0.0178
-------------+------------------------------           Adj R-squared =  0.0054
       Total |  15.5555556    80  .194444444           Root MSE      =  .43976

------------------------------------------------------------------------------
       water |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.1170732   .0977329    -1.20   0.235    -.3116057    .0774594
       _cons |   .3170732   .0686797     4.62   0.000     .1803696    .4537767
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.01
       Model |  .001758912     1  .001758912           Prob > F      =  0.9290
    Residual |  17.1857411    78  .220330014           R-squared     =  0.0001
-------------+------------------------------           Adj R-squared = -0.0127
       Total |     17.1875    79  .217563291           Root MSE      =  .46939

------------------------------------------------------------------------------
       water |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0093809   .1049923    -0.09   0.929    -.2184045    .1996428
       _cons |   .3170732   .0733069     4.33   0.000     .1711303     .463016
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.63
       Model |  .393352988     3  .131117663           Prob > F      =  0.5941
    Residual |  32.4637899   157  .206775732           R-squared     =  0.0120
-------------+------------------------------           Adj R-squared = -0.0069
       Total |  32.8571429   160  .205357143           Root MSE      =  .45473

------------------------------------------------------------------------------
       water |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   2.14e-16   .1004322     0.00   1.000    -.1983725    .1983725
     hotline |  -.1170732   .1010579    -1.16   0.248    -.3166817    .0825353
     verdade |  -.0093809   .1017116    -0.09   0.927    -.2102805    .1915188
       _cons |   .3170732   .0710163     4.46   0.000     .1768026    .4573437
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.63
            Prob > F =    0.5941
.59408203


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    0.30
       Model |  .048780488     1  .048780488           Prob > F      =  0.5828
    Residual |  12.8292683    80  .160365854           R-squared     =  0.0038
-------------+------------------------------           Adj R-squared = -0.0087
       Total |  12.8780488    81  .158988257           Root MSE      =  .40046

------------------------------------------------------------------------------
       sewer |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0487805   .0884462    -0.55   0.583     -.224794     .127233
       _cons |   .2195122   .0625409     3.51   0.001     .0950519    .3439725
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.64
       Model |  .097831978     1  .097831978           Prob > F      =  0.4270
    Residual |  12.1243902    79  .153473294           R-squared     =  0.0080
-------------+------------------------------           Adj R-squared = -0.0046
       Total |  12.2222222    80  .152777778           Root MSE      =  .39176

------------------------------------------------------------------------------
       sewer |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0695122   .0870637    -0.80   0.427    -.2428081    .1037837
       _cons |   .2195122   .0611821     3.59   0.001     .0977322    .3412921
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.20
       Model |  .032020013     1  .032020013           Prob > F      =  0.6595
    Residual |    12.76798    78  .163692051           R-squared     =  0.0025
-------------+------------------------------           Adj R-squared = -0.0103
       Total |        12.8    79  .162025316           Root MSE      =  .40459

------------------------------------------------------------------------------
       sewer |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   -.040025   .0904971    -0.44   0.660    -.2201909    .1401408
       _cons |   .2195122   .0631861     3.47   0.001     .0937182    .3453061
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.23
       Model |  .103539479     3   .03451316           Prob > F      =  0.8762
    Residual |   23.672858   157  .150782535           R-squared     =  0.0044
-------------+------------------------------           Adj R-squared = -0.0147
       Total |  23.7763975   160  .148602484           Root MSE      =  .38831

------------------------------------------------------------------------------
       sewer |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0487805   .0857627    -0.57   0.570    -.2181781    .1206171
     hotline |  -.0695122   .0862971    -0.81   0.422    -.2399653    .1009409
     verdade |   -.040025   .0868553    -0.46   0.646    -.2115806    .1315306
       _cons |   .2195122   .0606434     3.62   0.000       .09973    .3392944
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.23
            Prob > F =    0.8762
.87617312


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    2.63
       Model |  .597560976     1  .597560976           Prob > F      =  0.1085
    Residual |  18.1463415    80  .226829268           R-squared     =  0.0319
-------------+------------------------------           Adj R-squared =  0.0198
       Total |  18.7439024    81  .231406203           Root MSE      =  .47627

------------------------------------------------------------------------------
      health |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1707317   .1051896    -1.62   0.109    -.3800656    .0386022
       _cons |   .7317073   .0743802     9.84   0.000     .5836859    .8797287
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.31
       Model |  .065108401     1  .065108401           Prob > F      =  0.5819
    Residual |  16.8237805    79  .212959247           R-squared     =  0.0039
-------------+------------------------------           Adj R-squared = -0.0088
       Total |  16.8888889    80  .211111111           Root MSE      =  .46148

------------------------------------------------------------------------------
      health |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0567073   .1025578    -0.55   0.582    -.2608436    .1474289
       _cons |   .7317073   .0720703    10.15   0.000      .588255    .8751597
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    4.20
       Model |  .957629769     1  .957629769           Prob > F      =  0.0438
    Residual |  17.7923702    78  .228107311           R-squared     =  0.0511
-------------+------------------------------           Adj R-squared =  0.0389
       Total |       18.75    79  .237341772           Root MSE      =  .47761

------------------------------------------------------------------------------
      health |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.2188868   .1068293    -2.05   0.044    -.4315676    -.006206
       _cons |   .7317073   .0745895     9.81   0.000      .583211    .8802036
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    1.75
       Model |  1.22326755     3   .40775585           Prob > F      =  0.1598
    Residual |  36.6649312   157  .233534594           R-squared     =  0.0323
-------------+------------------------------           Adj R-squared =  0.0138
       Total |  37.8881988   160  .236801242           Root MSE      =  .48325

------------------------------------------------------------------------------
      health |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1707317    .106733    -1.60   0.112    -.3815496    .0400861
     hotline |  -.0567073    .107398    -0.53   0.598    -.2688387     .155424
     verdade |  -.2188868   .1080927    -2.02   0.045    -.4323903   -.0053833
       _cons |   .7317073   .0754716     9.70   0.000     .5826366     .880778
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    1.75
            Prob > F =    0.1598
.15983944


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    0.06
       Model |  .012195122     1  .012195122           Prob > F      =  0.8032
    Residual |  15.6097561    80  .195121951           R-squared     =  0.0008
-------------+------------------------------           Adj R-squared = -0.0117
       Total |  15.6219512    81  .192863595           Root MSE      =  .44173

------------------------------------------------------------------------------
  recreation |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0243902    .097561     0.25   0.803    -.1697623    .2185428
       _cons |   .7317073    .068986    10.61   0.000     .5944207    .8689939
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.31
       Model |  .065108401     1  .065108401           Prob > F      =  0.5819
    Residual |  16.8237805    79  .212959247           R-squared     =  0.0039
-------------+------------------------------           Adj R-squared = -0.0088
       Total |  16.8888889    80  .211111111           Root MSE      =  .46148

------------------------------------------------------------------------------
  recreation |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0567073   .1025578    -0.55   0.582    -.2608436    .1474289
       _cons |   .7317073   .0720703    10.15   0.000      .588255    .8751597
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.39
       Model |  .084552846     1  .084552846           Prob > F      =  0.5318
    Residual |  16.7154472    78  .214300605           R-squared     =  0.0050
-------------+------------------------------           Adj R-squared = -0.0077
       Total |        16.8    79  .212658228           Root MSE      =  .46293

------------------------------------------------------------------------------
  recreation |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0650407   .1035458    -0.63   0.532    -.2711845    .1411032
       _cons |   .7317073   .0722969    10.12   0.000     .5877752    .8756394
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.36
       Model |  .228080341     3   .07602678           Prob > F      =  0.7811
    Residual |  33.0514228   157  .210518616           R-squared     =  0.0069
-------------+------------------------------           Adj R-squared = -0.0121
       Total |  33.2795031   160  .207996894           Root MSE      =  .45882

------------------------------------------------------------------------------
  recreation |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0243902   .1013371     0.24   0.810    -.1757696    .2245501
     hotline |  -.0567073   .1019685    -0.56   0.579    -.2581143    .1446997
     verdade |  -.0650407    .102628    -0.63   0.527    -.2677504    .1376691
       _cons |   .7317073   .0716561    10.21   0.000     .5901729    .8732417
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.36
            Prob > F =    0.7811
.78114863


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    0.00
       Model |  9.7700e-15     1  9.7700e-15           Prob > F      =  1.0000
    Residual |   7.2195122    80  .090243902           R-squared     =  0.0000
-------------+------------------------------           Adj R-squared = -0.0125
       Total |   7.2195122    81   .08912978           Root MSE      =  .30041

------------------------------------------------------------------------------
      temple |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |          0   .0663486     0.00   1.000     -.132038     .132038
       _cons |    .902439   .0469156    19.24   0.000     .8090741     .995804
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.00
       Model |  .000120446     1  .000120446           Prob > F      =  0.9711
    Residual |   7.2097561    79  .091262735           R-squared     =  0.0000
-------------+------------------------------           Adj R-squared = -0.0126
       Total |  7.20987654    80  .090123457           Root MSE      =   .3021

------------------------------------------------------------------------------
      temple |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   -.002439   .0671378    -0.04   0.971    -.1360735    .1311955
       _cons |    .902439   .0471797    19.13   0.000     .8085303    .9963478
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.18
       Model |  .018769543     1  .018769543           Prob > F      =  0.6694
    Residual |  7.96873046    78  .102163211           R-squared     =  0.0023
-------------+------------------------------           Adj R-squared = -0.0104
       Total |      7.9875    79  .101107595           Root MSE      =  .31963

------------------------------------------------------------------------------
      temple |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0306442   .0714937    -0.43   0.669    -.1729773     .111689
       _cons |    .902439   .0499178    18.08   0.000     .8030603    1.001818
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.09
       Model |   .02648239     3  .008827463           Prob > F      =  0.9647
    Residual |  15.1784866   157  .096678258           R-squared     =  0.0017
-------------+------------------------------           Adj R-squared = -0.0173
       Total |  15.2049689   160  .095031056           Root MSE      =  .31093

------------------------------------------------------------------------------
      temple |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   2.30e-16   .0686732     0.00   1.000    -.1356426    .1356426
     hotline |   -.002439   .0691011    -0.04   0.972    -.1389268    .1340487
     verdade |  -.0306442   .0695481    -0.44   0.660    -.1680148    .1067265
       _cons |    .902439   .0485593    18.58   0.000     .8065252    .9983528
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.09
            Prob > F =    0.9647
.96474103


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    0.05
       Model |  .012195122     1  .012195122           Prob > F      =  0.8170
    Residual |   18.097561    80  .226219512           R-squared     =  0.0007
-------------+------------------------------           Adj R-squared = -0.0118
       Total |  18.1097561    81  .223577236           Root MSE      =  .47563

------------------------------------------------------------------------------
    meetroom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0243902   .1050481     0.23   0.817    -.1846621    .2334426
       _cons |   .3170732   .0742802     4.27   0.000     .1692508    .4648955
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.10
       Model |   .02195122     1   .02195122           Prob > F      =  0.7569
    Residual |  17.9780488    79  .227570238           R-squared     =  0.0012
-------------+------------------------------           Adj R-squared = -0.0114
       Total |          18    80        .225           Root MSE      =  .47704

------------------------------------------------------------------------------
    meetroom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0329268   .1060177     0.31   0.757    -.1780961    .2439498
       _cons |   .3170732   .0745016     4.26   0.000     .1687814     .465365
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.73
       Model |  .148874296     1  .148874296           Prob > F      =  0.3939
    Residual |  15.8011257    78  .202578535           R-squared     =  0.0093
-------------+------------------------------           Adj R-squared = -0.0034
       Total |       15.95    79  .201898734           Root MSE      =  .45009

------------------------------------------------------------------------------
    meetroom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0863039    .100674    -0.86   0.394    -.2867305    .1141227
       _cons |   .3170732   .0702918     4.51   0.000     .1771329    .4570134
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.54
       Model |  .351411791     3  .117137264           Prob > F      =  0.6563
    Residual |  34.1206379   157  .217328904           R-squared     =  0.0102
-------------+------------------------------           Adj R-squared = -0.0087
       Total |  34.4720497   160  .215450311           Root MSE      =  .46619

------------------------------------------------------------------------------
    meetroom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0243902   .1029631     0.24   0.813    -.1789815    .2277619
     hotline |   .0329268   .1036047     0.32   0.751     -.171712    .2375656
     verdade |  -.0863039   .1042748    -0.83   0.409    -.2922665    .1196586
       _cons |   .3170732   .0728059     4.36   0.000     .1732677    .4608787
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.54
            Prob > F =    0.6563
.65626623


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    1.13
       Model |  .195121951     1  .195121951           Prob > F      =  0.2917
    Residual |  13.8536585    80  .173170732           R-squared     =  0.0139
-------------+------------------------------           Adj R-squared =  0.0016
       Total |  14.0487805    81  .173441734           Root MSE      =  .41614

------------------------------------------------------------------------------
        road |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.097561   .0919095    -1.06   0.292    -.2804667    .0853447
       _cons |   .2682927   .0649898     4.13   0.000     .1389588    .3976265
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.20
       Model |  .037947907     1  .037947907           Prob > F      =  0.6563
    Residual |  15.0237805    79  .190174437           R-squared     =  0.0025
-------------+------------------------------           Adj R-squared = -0.0101
       Total |  15.0617284    80  .188271605           Root MSE      =  .43609

------------------------------------------------------------------------------
        road |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0432927   .0969163    -0.45   0.656    -.2361997    .1496143
       _cons |   .2682927   .0681058     3.94   0.000     .1327315    .4038539
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.15
       Model |  .031027205     1  .031027205           Prob > F      =  0.7015
    Residual |  16.3564728    78  .209698369           R-squared     =  0.0019
-------------+------------------------------           Adj R-squared = -0.0109
       Total |     16.3875    79  .207436709           Root MSE      =  .45793

------------------------------------------------------------------------------
        road |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0393996   .1024279     0.38   0.702    -.1645187    .2433179
       _cons |   .2682927   .0715164     3.75   0.000     .1259145    .4106709
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.75
       Model |  .416444187     3  .138814729           Prob > F      =  0.5251
    Residual |  29.1363508   157  .185581853           R-squared     =  0.0141
-------------+------------------------------           Adj R-squared = -0.0047
       Total |   29.552795   160  .184704969           Root MSE      =  .43079

------------------------------------------------------------------------------
        road |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.097561   .0951461    -1.03   0.307    -.2854924    .0903705
     hotline |  -.0432927   .0957389    -0.45   0.652    -.2323951    .1458097
     verdade |   .0393996   .0963582     0.41   0.683     -.150926    .2297252
       _cons |   .2682927   .0672784     3.99   0.000     .1354051    .4011803
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.75
            Prob > F =    0.5251
.52505322


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    0.68
       Model |  .012755743     1  .012755743           Prob > F      =  0.4136
    Residual |  1.51096425    80  .018887053           R-squared     =  0.0084
-------------+------------------------------           Adj R-squared = -0.0040
       Total |  1.52371999    81  .018811358           Root MSE      =  .13743

------------------------------------------------------------------------------
      eadrop |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0249446   .0303532    -0.82   0.414    -.0853495    .0354603
       _cons |   .3610865    .021463    16.82   0.000     .3183738    .4037992
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    1.68
       Model |  .042580604     1  .042580604           Prob > F      =  0.1993
    Residual |  2.00782554    79  .025415513           R-squared     =  0.0208
-------------+------------------------------           Adj R-squared =  0.0084
       Total |  2.05040615    80  .025630077           Root MSE      =  .15942

------------------------------------------------------------------------------
      eadrop |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0458592   .0354299    -1.29   0.199    -.1163807    .0246623
       _cons |   .3610865   .0248976    14.50   0.000      .311529    .4106439
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.12
       Model |  .002831536     1  .002831536           Prob > F      =  0.7315
    Residual |   1.8626965    78  .023880724           R-squared     =  0.0015
-------------+------------------------------           Adj R-squared = -0.0113
       Total |  1.86552803    79  .023614279           Root MSE      =  .15453

------------------------------------------------------------------------------
      eadrop |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0119023   .0345656    -0.34   0.732    -.0807172    .0569126
       _cons |   .3610865   .0241341    14.96   0.000     .3130391    .4091339
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.78
       Model |  .046643933     3  .015547978           Prob > F      =  0.5060
    Residual |  3.12368604   157  .019896089           R-squared     =  0.0147
-------------+------------------------------           Adj R-squared = -0.0041
       Total |  3.17032997   160  .019814562           Root MSE      =  .14105

------------------------------------------------------------------------------
      eadrop |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0249446   .0311535    -0.80   0.425    -.0864786    .0365895
     hotline |  -.0458592   .0313476    -1.46   0.145    -.1077767    .0160583
     verdade |  -.0119023   .0315504    -0.38   0.706    -.0742203    .0504156
       _cons |   .3610865   .0220289    16.39   0.000     .3175753    .4045976
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.78
            Prob > F =    0.5060
.50595753


note: results saved to balance.xml

. 
. global list2=""

. matrix define fpvalue=(fschoolbuild \ fpolicesta \ felectricity \ fwater \ fsewer \ fhealth \ 
> frecreation \ ftemple \ fmeetroom \ froad \ feadrop)

. matrix rownames fpvalue = "schoolbuild" "policesta" "electricity" "water" "sewer" "health" "re
> creation" "temple" "meetroom" "road" "eadrop"

. global list2="$list2" + " fpvalue"

. xml_tab $list2, save(balance.xml) append sheet("fpvalue ea") 


note: results saved to balance.xml

. estimates clear

. 
. ***************************************************************
. *****  BALANCE OF INDIVIDUAL DEMOGRAPHIC CHARACTERISTICS  *****
. ***************************************************************
. 
. global demo1="sex age head housen single marriedunion noschl informalschl lit prim5y sec10y"

. global demo2="chang macua lomue chuabo chironga maconde"

. global demo3="cathol protest muslim"

. global demo4="job agric com art man assal tea puboff stud dom"

. global demo5="house land cattle cel expenditure"

. global demo6="netmean_dist"

. 
. set more off

. 
. foreach i in $demo1 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0, cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & drops2==0, cluster(ea)
 14.         estimates store `i'_10
 15.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & drops2==0, cluster(ea)
 16.         estimates store `i'_11
 17.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & drops2==0, cluster(ea)
 18.         estimates store `i'_12
 19.         regress `i' civiceduc hotline verdade if time==0 & drops2==0, cluster(ea)
 20.         test civiceduc hotline verdade
 21.         scalar define f`i'_4=r(p)
 22.         display f`i'_4
 23. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3" + " `i'_10" + " `i'_11" + " `i'
> _12"
 24.         xml_tab $list1, below save(balance.xml) append sheet("bal `i'")
 25.         estimates clear
 26. 
. }

Linear regression                                      Number of obs =     901
                                                       F(  1,    81) =    0.84
                                                       Prob > F      =  0.3607
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .49668

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         sex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0281501   .0306239    -0.92   0.361    -.0890821     .032782
       _cons |   .4535398   .0213189    21.27   0.000      .411122    .4959577
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     888
                                                       F(  1,    80) =    0.06
                                                       Prob > F      =  0.8032
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .49871

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         sex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0074694   .0298672     0.25   0.803    -.0519683     .066907
       _cons |   .4535398   .0213207    21.27   0.000     .4111103    .4959693
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     881
                                                       F(  1,    79) =    0.17
                                                       Prob > F      =  0.6808
                                                       R-squared     =  0.0002
                                                       Root MSE      =   .4989

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         sex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0126606   .0306641     0.41   0.681    -.0483748    .0736961
       _cons |   .4535398   .0213224    21.27   0.000     .4110986    .4959811
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1766
                                                       F(  3,   160) =    0.69
                                                       Prob > F      =  0.5570
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .49794

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         sex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0281501   .0305406    -0.92   0.358    -.0884647    .0321646
     hotline |   .0074694   .0297834     0.25   0.802      -.05135    .0662887
     verdade |   .0126606   .0305756     0.41   0.679    -.0477231    .0730444
       _cons |   .4535398   .0212609    21.33   0.000     .4115517    .4955279
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.69
            Prob > F =    0.5570
.55695872

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    2.84
                                                       Prob > F      =  0.0957
                                                       R-squared     =  0.0047
                                                       Root MSE      =  .49132

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         sex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0670676   .0397903    -1.69   0.096    -.1462378    .0121025
       _cons |   .4429066   .0282466    15.68   0.000     .3867047    .4991085
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    0.16
                                                       Prob > F      =  0.6881
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .49842

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         sex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |    .016825   .0417574     0.40   0.688    -.0662748    .0999248
       _cons |   .4429066   .0282488    15.68   0.000     .3866897    .4991234
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     568
                                                       F(  1,    79) =    0.68
                                                       Prob > F      =  0.4138
                                                       R-squared     =  0.0011
                                                       Root MSE      =  .49895

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         sex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0337959    .041132     0.82   0.414    -.0480753    .1156671
       _cons |   .4429066   .0282518    15.68   0.000     .3866728    .4991403
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1164
                                                       F(  3,   160) =    2.38
                                                       Prob > F      =  0.0713
                                                       R-squared     =  0.0060
                                                       Root MSE      =  .49552

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         sex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0670676   .0396876    -1.69   0.093    -.1454468    .0113115
     hotline |    .016825   .0416465     0.40   0.687    -.0654228    .0990727
     verdade |   .0337959   .0410184     0.82   0.411    -.0472114    .1148033
       _cons |   .4429066   .0281738    15.72   0.000     .3872662     .498547
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    2.38
            Prob > F =    0.0713
.07126551


note: results saved to balance.xml

Linear regression                                      Number of obs =     890
                                                       F(  1,    81) =    0.95
                                                       Prob > F      =  0.3338
                                                       R-squared     =  0.0019
                                                       Root MSE      =  13.579

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -1.182114   1.215839    -0.97   0.334    -3.601252    1.237024
       _cons |   38.32063   .9420697    40.68   0.000      36.4462    40.19505
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     881
                                                       F(  1,    80) =    0.00
                                                       Prob > F      =  0.9490
                                                       R-squared     =  0.0000
                                                       Root MSE      =  13.828

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0826968   1.288316    -0.06   0.949    -2.646526    2.481133
       _cons |   38.32063   .9421469    40.67   0.000      36.4457    40.19556
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     871
                                                       F(  1,    79) =    1.92
                                                       Prob > F      =  0.1697
                                                       R-squared     =  0.0038
                                                       Root MSE      =  13.571

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -1.664157   1.200866    -1.39   0.170     -4.05442    .7261061
       _cons |   38.32063   .9422267    40.67   0.000     36.44517    40.19608
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1750
                                                       F(  3,   160) =    0.99
                                                       Prob > F      =  0.3984
                                                       R-squared     =  0.0027
                                                       Root MSE      =  13.577

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -1.182114   1.212531    -0.97   0.331    -3.576744    1.212516
     hotline |  -.0826968   1.284705    -0.06   0.949    -2.619864     2.45447
     verdade |  -1.664157   1.197399    -1.39   0.167    -4.028903    .7005883
       _cons |   38.32063   .9395068    40.79   0.000     36.46519    40.17606
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.99
            Prob > F =    0.3984
.39835792

Linear regression                                      Number of obs =     583
                                                       F(  1,    81) =    0.00
                                                       Prob > F      =  0.9897
                                                       R-squared     =  0.0000
                                                       Root MSE      =  13.252

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0162621   1.250258     0.01   0.990    -2.471359    2.503883
       _cons |   36.96516   .9612889    38.45   0.000     35.05249    38.87782
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     584
                                                       F(  1,    80) =    1.76
                                                       Prob > F      =  0.1882
                                                       R-squared     =  0.0046
                                                       Root MSE      =  13.723

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   1.854709   1.397361     1.33   0.188    -.9261287    4.635546
       _cons |   36.96516   .9613608    38.45   0.000     35.05199    38.87833
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     563
                                                       F(  1,    79) =    0.05
                                                       Prob > F      =  0.8160
                                                       R-squared     =  0.0001
                                                       Root MSE      =  13.339

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.3238524   1.387405    -0.23   0.816    -3.085414    2.437709
       _cons |   36.96516   .9614668    38.45   0.000     35.05141    38.87891
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1156
                                                       F(  3,   160) =    0.99
                                                       Prob > F      =  0.3983
                                                       R-squared     =  0.0040
                                                       Root MSE      =  13.637

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0162621   1.247037     0.01   0.990    -2.446514    2.479038
     hotline |   1.854709   1.393657     1.33   0.185    -.8976278    4.607045
     verdade |  -.3238524   1.383576    -0.23   0.815    -3.056278    2.408573
       _cons |   36.96516   .9588128    38.55   0.000      35.0716    38.85872
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.99
            Prob > F =    0.3983
.39825666


note: results saved to balance.xml

Linear regression                                      Number of obs =     901
                                                       F(  1,    81) =    0.07
                                                       Prob > F      =  0.7912
                                                       R-squared     =  0.0001
                                                       Root MSE      =   .4371

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        head |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0083667   .0315001    -0.27   0.791    -.0710419    .0543086
       _cons |   .7477876   .0261032    28.65   0.000     .6958505    .7997247
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     888
                                                       F(  1,    80) =    0.02
                                                       Prob > F      =  0.8894
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .43607

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        head |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0046683   .0334571    -0.14   0.889      -.07125    .0619134
       _cons |   .7477876   .0261054    28.64   0.000     .6958362     .799739
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     881
                                                       F(  1,    79) =    0.27
                                                       Prob > F      =  0.6048
                                                       R-squared     =  0.0004
                                                       Root MSE      =  .42992

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        head |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0167812   .0322999     0.52   0.605    -.0475103    .0810726
       _cons |   .7477876   .0261075    28.64   0.000     .6958219    .7997534
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1766
                                                       F(  3,   160) =    0.35
                                                       Prob > F      =  0.7888
                                                       R-squared     =  0.0005
                                                       Root MSE      =  .43421

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        head |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0083667   .0314143    -0.27   0.790    -.0704069    .0536735
     hotline |  -.0046683   .0333632    -0.14   0.889    -.0705574    .0612207
     verdade |   .0167812   .0322067     0.52   0.603    -.0468238    .0803861
       _cons |   .7477876   .0260321    28.73   0.000     .6963767    .7991985
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.35
            Prob > F =    0.7888
.78883371

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    0.20
                                                       Prob > F      =  0.6541
                                                       R-squared     =  0.0005
                                                       Root MSE      =  .44239

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        head |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0191124   .0424918    -0.45   0.654    -.1036577    .0654329
       _cons |   .7439446   .0358947    20.73   0.000     .6725255    .8153637
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    0.05
                                                       Prob > F      =  0.8318
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .43971

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        head |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0090453    .042461    -0.21   0.832    -.0935454    .0754548
       _cons |   .7439446   .0358974    20.72   0.000     .6725065    .8153827
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     568
                                                       F(  1,    79) =    0.88
                                                       Prob > F      =  0.3505
                                                       R-squared     =  0.0023
                                                       Root MSE      =  .42482

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        head |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0410016   .0436601     0.94   0.351    -.0459016    .1279048
       _cons |   .7439446   .0359012    20.72   0.000      .672485    .8154042
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1164
                                                       F(  3,   160) =    1.20
                                                       Prob > F      =  0.3134
                                                       R-squared     =  0.0027
                                                       Root MSE      =  .43514

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        head |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0191124   .0423822    -0.45   0.653    -.1028131    .0645883
     hotline |  -.0090453   .0423483    -0.21   0.831     -.092679    .0745884
     verdade |   .0410016   .0435395     0.94   0.348    -.0449846    .1269878
       _cons |   .7439446   .0358021    20.78   0.000      .673239    .8146502
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.20
            Prob > F =    0.3134
.31342006


note: results saved to balance.xml

Linear regression                                      Number of obs =     900
                                                       F(  1,    81) =    1.61
                                                       Prob > F      =  0.2084
                                                       R-squared     =  0.0035
                                                       Root MSE      =  2.7784

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      housen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .3306436   .2607435     1.27   0.208    -.1881542    .8494413
       _cons |    5.65708   .1472578    38.42   0.000     5.364083    5.950076
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     888
                                                       F(  1,    80) =    2.56
                                                       Prob > F      =  0.1136
                                                       R-squared     =  0.0043
                                                       Root MSE      =  2.6685

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      housen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .3520947   .2201238     1.60   0.114    -.0859656    .7901549
       _cons |    5.65708   .1472701    38.41   0.000     5.364003    5.950157
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     881
                                                       F(  1,    79) =    0.58
                                                       Prob > F      =  0.4489
                                                       R-squared     =  0.0010
                                                       Root MSE      =   2.763

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      housen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .1774192   .2331138     0.76   0.449    -.2865822    .6414206
       _cons |    5.65708   .1472823    38.41   0.000     5.363922    5.950238
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1765
                                                       F(  3,   160) =    1.03
                                                       Prob > F      =  0.3801
                                                       R-squared     =  0.0025
                                                       Root MSE      =   2.847

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      housen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .3306436   .2600339     1.27   0.205    -.1828977    .8441849
     hotline |   .3520947   .2195063     1.60   0.111    -.0814087     .785598
     verdade |   .1774192   .2324407     0.76   0.446    -.2816283    .6364667
       _cons |    5.65708    .146857    38.52   0.000     5.367051    5.947108
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.03
            Prob > F =    0.3801
.38014489

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    0.60
                                                       Prob > F      =  0.4415
                                                       R-squared     =  0.0016
                                                       Root MSE      =  2.5773

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      housen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .2069796   .2675787     0.77   0.441     -.325418    .7393773
       _cons |   5.757785   .1674006    34.40   0.000     5.424711     6.09086
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    2.90
                                                       Prob > F      =  0.0927
                                                       R-squared     =  0.0070
                                                       Root MSE      =  2.5594

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      housen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |    .430134   .2527978     1.70   0.093    -.0729496    .9332176
       _cons |   5.757785   .1674133    34.39   0.000     5.424622    6.090949
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     568
                                                       F(  1,    79) =    0.55
                                                       Prob > F      =  0.4622
                                                       R-squared     =  0.0013
                                                       Root MSE      =  2.7137

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      housen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .1992038   .2696124     0.74   0.462    -.3374462    .7358538
       _cons |   5.757785   .1674312    34.39   0.000     5.424522    6.091049
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1164
                                                       F(  3,   160) =    0.97
                                                       Prob > F      =  0.4074
                                                       R-squared     =  0.0031
                                                       Root MSE      =  2.7588

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      housen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .2069796   .2668886     0.78   0.439    -.3200991    .7340584
     hotline |    .430134   .2521266     1.71   0.090    -.0677912    .9280592
     verdade |   .1992038   .2688678     0.74   0.460    -.3317837    .7301913
       _cons |   5.757785   .1669689    34.48   0.000     5.428038    6.087533
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.97
            Prob > F =    0.4074
.40737714


note: results saved to balance.xml

Linear regression                                      Number of obs =     901
                                                       F(  1,    81) =    0.38
                                                       Prob > F      =  0.5399
                                                       R-squared     =  0.0005
                                                       Root MSE      =  .37773

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      single |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0166841   .0271041     0.62   0.540    -.0372445    .0706127
       _cons |   .1637168   .0211186     7.75   0.000     .1216975    .2057362
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     888
                                                       F(  1,    80) =    0.52
                                                       Prob > F      =  0.4739
                                                       R-squared     =  0.0007
                                                       Root MSE      =  .37891

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      single |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0197694   .0274727     0.72   0.474     -.034903    .0744418
       _cons |   .1637168   .0211204     7.75   0.000      .121686    .2057477
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     881
                                                       F(  1,    79) =    0.37
                                                       Prob > F      =  0.5436
                                                       R-squared     =  0.0006
                                                       Root MSE      =  .37816

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      single |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0181014   .0296741     0.61   0.544    -.0409634    .0771661
       _cons |   .1637168   .0211221     7.75   0.000     .1216743    .2057593
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1766
                                                       F(  3,   160) =    0.20
                                                       Prob > F      =  0.8937
                                                       R-squared     =  0.0004
                                                       Root MSE      =  .38222

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      single |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0166841   .0270303     0.62   0.538    -.0366981    .0700663
     hotline |   .0197694   .0273956     0.72   0.472    -.0343342    .0738731
     verdade |   .0181014   .0295884     0.61   0.542    -.0403327    .0765355
       _cons |   .1637168   .0210611     7.77   0.000     .1221232    .2053104
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.20
            Prob > F =    0.8937
.89372063

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    0.69
                                                       Prob > F      =  0.4080
                                                       R-squared     =  0.0015
                                                       Root MSE      =  .38074

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      single |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0292376   .0351502    -0.83   0.408    -.0991754    .0407002
       _cons |   .1903114   .0285591     6.66   0.000     .1334879     .247135
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    0.00
                                                       Prob > F      =  0.9795
                                                       R-squared     =  0.0000
                                                       Root MSE      =   .3936

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      single |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0009637   .0374512     0.03   0.980    -.0735665     .075494
       _cons |   .1903114   .0285612     6.66   0.000     .1334728    .2471501
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     568
                                                       F(  1,    79) =    0.73
                                                       Prob > F      =  0.3958
                                                       R-squared     =  0.0018
                                                       Root MSE      =  .37968

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      single |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0326053   .0381888    -0.85   0.396    -.1086182    .0434075
       _cons |   .1903114   .0285643     6.66   0.000     .1334556    .2471672
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1164
                                                       F(  3,   160) =    0.55
                                                       Prob > F      =  0.6493
                                                       R-squared     =  0.0017
                                                       Root MSE      =  .38052

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      single |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0292376   .0350595    -0.83   0.406    -.0984767    .0400015
     hotline |   .0009637   .0373518     0.03   0.979    -.0728023    .0747298
     verdade |  -.0326053   .0380833    -0.86   0.393    -.1078161    .0426055
       _cons |   .1903114   .0284854     6.68   0.000     .1340555    .2465673
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.55
            Prob > F =    0.6493
.64929308


note: results saved to balance.xml

Linear regression                                      Number of obs =     901
                                                       F(  1,    81) =    0.00
                                                       Prob > F      =  0.9923
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .45097

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
marriedunion |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0003351   .0344506     0.01   0.992    -.0682109     .068881
       _cons |   .7168142   .0230084    31.15   0.000     .6710347    .7625936
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     888
                                                       F(  1,    80) =    0.05
                                                       Prob > F      =  0.8193
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .44913

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
marriedunion |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0079565   .0347201     0.23   0.819    -.0611387    .0770517
       _cons |   .7168142   .0230103    31.15   0.000     .6710222    .7626061
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     881
                                                       F(  1,    79) =    0.57
                                                       Prob > F      =  0.4528
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .44496

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
marriedunion |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0244446   .0323954     0.75   0.453    -.0400369     .088926
       _cons |   .7168142   .0230122    31.15   0.000     .6710095    .7626188
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1766
                                                       F(  3,   160) =    0.24
                                                       Prob > F      =  0.8657
                                                       R-squared     =  0.0005
                                                       Root MSE      =  .44701

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
marriedunion |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0003351   .0343569     0.01   0.992    -.0675164    .0681865
     hotline |   .0079565   .0346227     0.23   0.819      -.06042    .0763329
     verdade |   .0244446   .0323019     0.76   0.450    -.0393484    .0882376
       _cons |   .7168142   .0229458    31.24   0.000     .6714985    .7621298
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.24
            Prob > F =    0.8657
.86570723

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    0.81
                                                       Prob > F      =  0.3708
                                                       R-squared     =  0.0019
                                                       Root MSE      =  .45626

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
marriedunion |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0397111   .0441287     0.90   0.371    -.0480912    .1275134
       _cons |   .6851211   .0294695    23.25   0.000     .6264861    .7437562
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    0.65
                                                       Prob > F      =  0.4210
                                                       R-squared     =  0.0016
                                                       Root MSE      =   .4571

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
marriedunion |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0363554    .044945     0.81   0.421     -.053088    .1257988
       _cons |   .6851211   .0294717    23.25   0.000     .6264705    .7437717
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     568
                                                       F(  1,    79) =    3.93
                                                       Prob > F      =  0.0508
                                                       R-squared     =  0.0084
                                                       Root MSE      =  .44524

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
marriedunion |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |    .081904   .0412986     1.98   0.051    -.0002989    .1641068
       _cons |   .6851211   .0294749    23.24   0.000     .6264528    .7437894
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1164
                                                       F(  3,   160) =    1.32
                                                       Prob > F      =  0.2683
                                                       R-squared     =  0.0041
                                                       Root MSE      =  .44675

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
marriedunion |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0397111   .0440149     0.90   0.368     -.047214    .1266362
     hotline |   .0363554   .0448257     0.81   0.419    -.0521709    .1248817
     verdade |    .081904   .0411846     1.99   0.048     .0005685    .1632395
       _cons |   .6851211   .0293935    23.31   0.000     .6270718    .7431704
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.32
            Prob > F =    0.2683
.26832769


note: results saved to balance.xml

Linear regression                                      Number of obs =     901
                                                       F(  1,    81) =    0.36
                                                       Prob > F      =  0.5478
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .40663

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      noschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0235873    .039081     0.60   0.548    -.0541715    .1013462
       _cons |   .1969027   .0242087     8.13   0.000      .148735    .2450703
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     886
                                                       F(  1,    80) =    0.21
                                                       Prob > F      =  0.6446
                                                       R-squared     =  0.0005
                                                       Root MSE      =  .39145

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      noschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0171792   .0370961    -0.46   0.645    -.0910027    .0566444
       _cons |   .1969027   .0242107     8.13   0.000     .1487218    .2450835
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     880
                                                       F(  1,    79) =    1.23
                                                       Prob > F      =  0.2710
                                                       R-squared     =  0.0019
                                                       Root MSE      =  .38483

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      noschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0333513   .0300833    -1.11   0.271    -.0932305     .026528
       _cons |   .1969027   .0242127     8.13   0.000     .1487085    .2450968
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1763
                                                       F(  3,   160) =    1.01
                                                       Prob > F      =  0.3884
                                                       R-squared     =  0.0029
                                                       Root MSE      =  .39264

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      noschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0235873   .0389747     0.61   0.546    -.0533838    .1005584
     hotline |  -.0171792    .036992    -0.46   0.643    -.0902347    .0558764
     verdade |  -.0333513   .0299964    -1.11   0.268    -.0925912    .0258887
       _cons |   .1969027   .0241428     8.16   0.000      .149223    .2445823
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.01
            Prob > F =    0.3884
.38844983

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    1.43
                                                       Prob > F      =  0.2360
                                                       R-squared     =  0.0037
                                                       Root MSE      =  .39814

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      noschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0484661   .0405944     1.19   0.236    -.0323039    .1292362
       _cons |   .1730104   .0269788     6.41   0.000     .1193311    .2266897
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     586
                                                       F(  1,    80) =    0.10
                                                       Prob > F      =  0.7564
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .38411

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      noschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0121748   .0391164     0.31   0.756    -.0656692    .0900188
       _cons |   .1730104   .0269809     6.41   0.000     .1193167     .226704
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  1,    79) =    0.31
                                                       Prob > F      =  0.5768
                                                       R-squared     =  0.0006
                                                       Root MSE      =  .37084

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      noschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0183341   .0327127    -0.56   0.577    -.0834471    .0467789
       _cons |   .1730104   .0269838     6.41   0.000     .1193005    .2267202
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1162
                                                       F(  3,   160) =    1.24
                                                       Prob > F      =  0.2983
                                                       R-squared     =  0.0040
                                                       Root MSE      =  .38752

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      noschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0484661   .0404898     1.20   0.233    -.0314972    .1284294
     hotline |   .0121748   .0390125     0.31   0.755    -.0648711    .0892207
     verdade |  -.0183341   .0326224    -0.56   0.575    -.0827601    .0460919
       _cons |   .1730104   .0269093     6.43   0.000     .1198672    .2261535
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.24
            Prob > F =    0.2983
.29830559


note: results saved to balance.xml

Linear regression                                      Number of obs =     901
                                                       F(  1,    81) =    0.01
                                                       Prob > F      =  0.9315
                                                       R-squared     =  0.0000
                                                       Root MSE      =   .2553

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
informalschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0017541   .0203362    -0.09   0.931    -.0422168    .0387085
       _cons |   .0707965   .0143399     4.94   0.000     .0422646    .0993283
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     886
                                                       F(  1,    80) =    0.18
                                                       Prob > F      =  0.6749
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .24956

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
informalschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0085845    .020394    -0.42   0.675    -.0491698    .0320009
       _cons |   .0707965   .0143411     4.94   0.000     .0422567    .0993362
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     880
                                                       F(  1,    79) =    0.01
                                                       Prob > F      =  0.9364
                                                       R-squared     =  0.0000
                                                       Root MSE      =   .2581

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
informalschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0016334   .0204147     0.08   0.936    -.0390009    .0422678
       _cons |   .0707965   .0143423     4.94   0.000     .0422488    .0993441
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1763
                                                       F(  3,   160) =    0.10
                                                       Prob > F      =  0.9619
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .25309

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
informalschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0017541   .0202809    -0.09   0.931    -.0418069    .0382986
     hotline |  -.0085845   .0203368    -0.42   0.674    -.0487476    .0315787
     verdade |   .0016334   .0203557     0.08   0.936    -.0385671     .041834
       _cons |   .0707965   .0143009     4.95   0.000     .0425536    .0990393
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.10
            Prob > F =    0.9619
.961853

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    0.93
                                                       Prob > F      =  0.3381
                                                       R-squared     =  0.0020
                                                       Root MSE      =  .25217

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
informalschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0225378   .0233886    -0.96   0.338    -.0690738    .0239982
       _cons |   .0795848   .0183573     4.34   0.000     .0430595      .11611
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     586
                                                       F(  1,    80) =    0.05
                                                       Prob > F      =  0.8251
                                                       R-squared     =  0.0001
                                                       Root MSE      =   .2667

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
informalschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0055107   .0248522    -0.22   0.825    -.0549682    .0439468
       _cons |   .0795848   .0183587     4.33   0.000     .0430498    .1161198
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  1,    79) =    0.03
                                                       Prob > F      =  0.8734
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .26801

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
informalschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0040452   .0252969    -0.16   0.873    -.0543974     .046307
       _cons |   .0795848   .0183607     4.33   0.000     .0430388    .1161308
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1162
                                                       F(  3,   160) =    0.41
                                                       Prob > F      =  0.7440
                                                       R-squared     =  0.0011
                                                       Root MSE      =  .25784

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
informalschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0225378   .0233284    -0.97   0.335     -.068609    .0235334
     hotline |  -.0055107   .0247863    -0.22   0.824    -.0544611    .0434397
     verdade |  -.0040452   .0252271    -0.16   0.873    -.0538661    .0457757
       _cons |   .0795848     .01831     4.35   0.000     .0434244    .1157452
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.41
            Prob > F =    0.7440
.74404862


note: results saved to balance.xml

Linear regression                                      Number of obs =     901
                                                       F(  1,    81) =    0.36
                                                       Prob > F      =  0.5478
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .40663

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         lit |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0235873    .039081    -0.60   0.548    -.1013462    .0541715
       _cons |   .8030973   .0242087    33.17   0.000     .7549297     .851265
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     886
                                                       F(  1,    80) =    0.21
                                                       Prob > F      =  0.6446
                                                       R-squared     =  0.0005
                                                       Root MSE      =  .39145

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         lit |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0171792   .0370961     0.46   0.645    -.0566444    .0910027
       _cons |   .8030973   .0242107    33.17   0.000     .7549165    .8512782
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     880
                                                       F(  1,    79) =    1.23
                                                       Prob > F      =  0.2710
                                                       R-squared     =  0.0019
                                                       Root MSE      =  .38483

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         lit |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0333513   .0300833     1.11   0.271     -.026528    .0932305
       _cons |   .8030973   .0242127    33.17   0.000     .7549032    .8512915
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1763
                                                       F(  3,   160) =    1.01
                                                       Prob > F      =  0.3884
                                                       R-squared     =  0.0029
                                                       Root MSE      =  .39264

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         lit |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0235873   .0389747    -0.61   0.546    -.1005584    .0533838
     hotline |   .0171792    .036992     0.46   0.643    -.0558764    .0902347
     verdade |   .0333513   .0299964     1.11   0.268    -.0258887    .0925912
       _cons |   .8030973   .0241428    33.26   0.000     .7554177     .850777
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.01
            Prob > F =    0.3884
.38844983

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    1.43
                                                       Prob > F      =  0.2360
                                                       R-squared     =  0.0037
                                                       Root MSE      =  .39814

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         lit |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0484661   .0405944    -1.19   0.236    -.1292362    .0323039
       _cons |   .8269896   .0269788    30.65   0.000     .7733103    .8806689
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     586
                                                       F(  1,    80) =    0.10
                                                       Prob > F      =  0.7564
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .38411

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         lit |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0121748   .0391164    -0.31   0.756    -.0900188    .0656692
       _cons |   .8269896   .0269809    30.65   0.000      .773296    .8806833
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  1,    79) =    0.31
                                                       Prob > F      =  0.5768
                                                       R-squared     =  0.0006
                                                       Root MSE      =  .37084

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         lit |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0183341   .0327127     0.56   0.577    -.0467789    .0834471
       _cons |   .8269896   .0269838    30.65   0.000     .7732798    .8806995
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1162
                                                       F(  3,   160) =    1.24
                                                       Prob > F      =  0.2983
                                                       R-squared     =  0.0040
                                                       Root MSE      =  .38752

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         lit |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0484661   .0404898    -1.20   0.233    -.1284294    .0314972
     hotline |  -.0121748   .0390125    -0.31   0.755    -.0892207    .0648711
     verdade |   .0183341   .0326224     0.56   0.575    -.0460919    .0827601
       _cons |   .8269896   .0269093    30.73   0.000     .7738465    .8801328
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.24
            Prob > F =    0.2983
.29830559


note: results saved to balance.xml

Linear regression                                      Number of obs =     901
                                                       F(  1,    81) =    0.15
                                                       Prob > F      =  0.6997
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .45407

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      prim5y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .013028     .03365     0.39   0.700    -.0539249    .0799809
       _cons |   .2831858   .0218778    12.94   0.000     .2396559    .3267158
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     886
                                                       F(  1,    80) =    0.97
                                                       Prob > F      =  0.3274
                                                       R-squared     =  0.0011
                                                       Root MSE      =  .44349

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      prim5y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0297296   .0301689    -0.99   0.327    -.0897677    .0303084
       _cons |   .2831858   .0218797    12.94   0.000     .2396439    .3267278
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     880
                                                       F(  1,    79) =    0.44
                                                       Prob > F      =  0.5104
                                                       R-squared     =  0.0006
                                                       Root MSE      =  .45611

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      prim5y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0228889   .0346176     0.66   0.510    -.0460157    .0917936
       _cons |   .2831858   .0218815    12.94   0.000     .2396319    .3267398
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1763
                                                       F(  3,   160) =    1.00
                                                       Prob > F      =  0.3925
                                                       R-squared     =  0.0019
                                                       Root MSE      =  .45138

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      prim5y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .013028   .0335584     0.39   0.698    -.0532467    .0793026
     hotline |  -.0297296   .0300843    -0.99   0.325    -.0891431    .0296839
     verdade |   .0228889   .0345177     0.66   0.508    -.0452801     .091058
       _cons |   .2831858   .0218183    12.98   0.000     .2400969    .3262748
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.00
            Prob > F =    0.3925
.39250423

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    0.00
                                                       Prob > F      =  0.9709
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .45194

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      prim5y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0014979   .0409614     0.04   0.971    -.0800025    .0829982
       _cons |    .283737   .0300503     9.44   0.000     .2239464    .3435276
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     586
                                                       F(  1,    80) =    0.39
                                                       Prob > F      =  0.5342
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .44524

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      prim5y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0244778   .0392072    -0.62   0.534    -.1025027    .0535471
       _cons |    .283737   .0300526     9.44   0.000     .2239305    .3435436
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  1,    79) =    0.89
                                                       Prob > F      =  0.3471
                                                       R-squared     =  0.0019
                                                       Root MSE      =  .46008

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      prim5y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |    .040004   .0422895     0.95   0.347    -.0441712    .1241791
       _cons |    .283737   .0300558     9.44   0.000     .2239125    .3435616
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1162
                                                       F(  3,   160) =    0.92
                                                       Prob > F      =  0.4332
                                                       R-squared     =  0.0026
                                                       Root MSE      =  .45277

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      prim5y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0014979   .0408559     0.04   0.971    -.0791884    .0821842
     hotline |  -.0244778   .0391032    -0.63   0.532    -.1017027    .0527472
     verdade |    .040004   .0421727     0.95   0.344     -.043283     .123291
       _cons |    .283737   .0299728     9.47   0.000     .2245437    .3429304
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.92
            Prob > F =    0.4332
.43317247


note: results saved to balance.xml

Linear regression                                      Number of obs =     901
                                                       F(  1,    81) =    0.11
                                                       Prob > F      =  0.7457
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .36578

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      sec10y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.010042   .0308555    -0.33   0.746    -.0714347    .0513508
       _cons |   .1637168   .0225199     7.27   0.000     .1189093    .2085244
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     886
                                                       F(  1,    80) =    0.13
                                                       Prob > F      =  0.7169
                                                       R-squared     =  0.0002
                                                       Root MSE      =   .3754

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      sec10y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0113984   .0313274     0.36   0.717     -.050945    .0737418
       _cons |   .1637168   .0225218     7.27   0.000     .1188969    .2085367
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     880
                                                       F(  1,    79) =    0.44
                                                       Prob > F      =  0.5106
                                                       R-squared     =  0.0009
                                                       Root MSE      =  .36064

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      sec10y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0211934   .0320679    -0.66   0.511    -.0850231    .0426362
       _cons |   .1637168   .0225237     7.27   0.000     .1188846    .2085491
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1763
                                                       F(  3,   160) =    0.39
                                                       Prob > F      =  0.7572
                                                       R-squared     =  0.0011
                                                       Root MSE      =  .36573

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      sec10y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.010042   .0307716    -0.33   0.745    -.0708128    .0507288
     hotline |   .0113984   .0312395     0.36   0.716    -.0502965    .0730933
     verdade |  -.0211934   .0319754    -0.66   0.508    -.0843416    .0419547
       _cons |   .1637168   .0224586     7.29   0.000     .1193632    .2080704
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.39
            Prob > F =    0.7572
.75724306

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    0.06
                                                       Prob > F      =  0.8037
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .36891

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      sec10y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0083718   .0335745    -0.25   0.804    -.0751745    .0584308
       _cons |     .16609   .0248661     6.68   0.000     .1166143    .2155657
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     586
                                                       F(  1,    80) =    0.00
                                                       Prob > F      =  0.9478
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .37382

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      sec10y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0022602   .0344259     0.07   0.948    -.0662495    .0707699
       _cons |     .16609    .024868     6.68   0.000      .116601    .2155789
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  1,    79) =    0.40
                                                       Prob > F      =  0.5301
                                                       R-squared     =  0.0009
                                                       Root MSE      =  .36257

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      sec10y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0222051    .035209    -0.63   0.530    -.0922868    .0478766
       _cons |     .16609   .0248707     6.68   0.000     .1165861    .2155938
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1162
                                                       F(  3,   160) =    0.20
                                                       Prob > F      =  0.8940
                                                       R-squared     =  0.0007
                                                       Root MSE      =  .36638

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      sec10y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0083718    .033488    -0.25   0.803    -.0745073    .0577636
     hotline |   .0022602   .0343345     0.07   0.948    -.0655471    .0700675
     verdade |  -.0222051   .0351118    -0.63   0.528    -.0915473    .0471372
       _cons |     .16609    .024802     6.70   0.000     .1171084    .2150715
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.20
            Prob > F =    0.8940
.89402895


note: results saved to balance.xml

. 
. foreach i in $demo2 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0, cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & drops2==0, cluster(ea)
 14.         estimates store `i'_10
 15.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & drops2==0, cluster(ea)
 16.         estimates store `i'_11
 17.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & drops2==0, cluster(ea)
 18.         estimates store `i'_12
 19.         regress `i' civiceduc hotline verdade if time==0 & drops2==0, cluster(ea)
 20.         test civiceduc hotline verdade
 21.         scalar define f`i'_4=r(p)
 22.         display f`i'_4
 23. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3" + " `i'_10" + " `i'_11" + " `i'
> _12"
 24.         xml_tab $list1, below save(balance.xml) append sheet("bal `i'")
 25.         estimates clear
 26. 
. }

Linear regression                                      Number of obs =     898
                                                       F(  1,    81) =    0.10
                                                       Prob > F      =  0.7498
                                                       R-squared     =  0.0009
                                                       Root MSE      =  .47924

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       chang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0283135   .0884897     0.32   0.750    -.1477532    .2043801
       _cons |   .3422222   .0623856     5.49   0.000     .2180944    .4663501
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     886
                                                       F(  1,    80) =    0.01
                                                       Prob > F      =  0.9213
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .47637

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       chang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0086952   .0877129     0.10   0.921    -.1658591    .1832495
       _cons |   .3422222   .0623909     5.49   0.000     .2180604     .466384
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     878
                                                       F(  1,    79) =    0.10
                                                       Prob > F      =  0.7491
                                                       R-squared     =  0.0009
                                                       Root MSE      =  .47929

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       chang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0292731    .091214     0.32   0.749    -.1522839    .2108301
       _cons |   .3422222   .0623961     5.48   0.000      .218026    .4664185
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1762
                                                       F(  3,   160) =    0.05
                                                       Prob > F      =  0.9839
                                                       R-squared     =  0.0007
                                                       Root MSE      =  .47999

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       chang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0283135   .0882489     0.32   0.749    -.1459693    .2025963
     hotline |   .0086952   .0874669     0.10   0.921    -.1640434    .1814338
     verdade |   .0292731   .0909506     0.32   0.748    -.1503454    .2088916
       _cons |   .3422222   .0622159     5.50   0.000      .219352    .4650925
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.05
            Prob > F =    0.9839
.98386116

Linear regression                                      Number of obs =     586
                                                       F(  1,    81) =    0.02
                                                       Prob > F      =  0.8767
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .47978

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       chang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0141554   .0909813     0.16   0.877    -.1668689    .1951797
       _cons |    .349481   .0642961     5.44   0.000     .2215519      .47741
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    0.03
                                                       Prob > F      =  0.8609
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .48009

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       chang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0162908    .092692     0.18   0.861     -.168172    .2007537
       _cons |    .349481   .0643009     5.44   0.000     .2215181    .4774438
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  1,    79) =    0.05
                                                       Prob > F      =  0.8237
                                                       R-squared     =  0.0005
                                                       Root MSE      =  .48067

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       chang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0210226   .0940666     0.22   0.824    -.1662123    .2082575
       _cons |    .349481   .0643079     5.43   0.000     .2214794    .4774826
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1162
                                                       F(  3,   160) =    0.02
                                                       Prob > F      =  0.9964
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .48143

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       chang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0141554   .0907468     0.16   0.876    -.1650605    .1933713
     hotline |   .0162908   .0924461     0.18   0.860     -.166281    .1988627
     verdade |   .0210226   .0938069     0.22   0.823    -.1642368     .206282
       _cons |    .349481   .0641303     5.45   0.000     .2228299     .476132
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.02
            Prob > F =    0.9964
.99638973


note: results saved to balance.xml

Linear regression                                      Number of obs =     898
                                                       F(  1,    81) =    0.14
                                                       Prob > F      =  0.7102
                                                       R-squared     =  0.0013
                                                       Root MSE      =  .41172

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       macua |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0302183   .0810257    -0.37   0.710    -.1914341    .1309975
       _cons |   .2311111   .0578098     4.00   0.000     .1160878    .3461345
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     886
                                                       F(  1,    80) =    0.01
                                                       Prob > F      =  0.9380
                                                       R-squared     =  0.0001
                                                       Root MSE      =     .42

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       macua |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0063405   .0812115    -0.08   0.938    -.1679565    .1552756
       _cons |   .2311111   .0578146     4.00   0.000     .1160563    .3461659
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     878
                                                       F(  1,    79) =    0.15
                                                       Prob > F      =  0.7019
                                                       R-squared     =  0.0013
                                                       Root MSE      =  .41198

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       macua |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0301765   .0785522    -0.38   0.702    -.1865308    .1261777
       _cons |   .2311111   .0578195     4.00   0.000     .1160244    .3461979
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1762
                                                       F(  3,   160) =    0.08
                                                       Prob > F      =  0.9711
                                                       R-squared     =  0.0011
                                                       Root MSE      =  .41073

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       macua |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0302183   .0808052    -0.37   0.709    -.1898007    .1293641
     hotline |  -.0063405   .0809837    -0.08   0.938    -.1662754    .1535944
     verdade |  -.0301765   .0783254    -0.39   0.701    -.1848614    .1245083
       _cons |   .2311111   .0576525     4.01   0.000     .1172531    .3449691
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.08
            Prob > F =    0.9711
.97109797

Linear regression                                      Number of obs =     586
                                                       F(  1,    81) =    0.19
                                                       Prob > F      =  0.6644
                                                       R-squared     =  0.0020
                                                       Root MSE      =  .41693

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       macua |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0368273   .0845712    -0.44   0.664    -.2050975    .1314428
       _cons |   .2422145   .0599657     4.04   0.000     .1229016    .3615275
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    0.01
                                                       Prob > F      =  0.9324
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .42688

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       macua |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0073152   .0860161    -0.09   0.932    -.1784926    .1638622
       _cons |   .2422145   .0599702     4.04   0.000     .1228701     .361559
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  1,    79) =    0.21
                                                       Prob > F      =  0.6517
                                                       R-squared     =  0.0020
                                                       Root MSE      =  .41723

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       macua |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0371786   .0820422    -0.45   0.652    -.2004795    .1261224
       _cons |   .2422145   .0599767     4.04   0.000     .1228339    .3615951
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1162
                                                       F(  3,   160) =    0.11
                                                       Prob > F      =  0.9546
                                                       R-squared     =  0.0016
                                                       Root MSE      =  .41599

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       macua |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0368273   .0843532    -0.44   0.663    -.2034165    .1297619
     hotline |  -.0073152   .0857879    -0.09   0.932    -.1767378    .1621074
     verdade |  -.0371786   .0818157    -0.45   0.650    -.1987565    .1243994
       _cons |   .2422145   .0598111     4.05   0.000     .1240935    .3603356
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.11
            Prob > F =    0.9546
.95463302


note: results saved to balance.xml

Linear regression                                      Number of obs =     898
                                                       F(  1,    81) =    0.02
                                                       Prob > F      =  0.8803
                                                       R-squared     =  0.0002
                                                       Root MSE      =   .3006

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       lomue |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0084623   .0559994    -0.15   0.880    -.1198836     .102959
       _cons |   .1044444   .0422503     2.47   0.016     .0203797    .1885092
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     886
                                                       F(  1,    80) =    0.37
                                                       Prob > F      =  0.5422
                                                       R-squared     =  0.0030
                                                       Root MSE      =  .28488

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       lomue |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0310499   .0507232    -0.61   0.542    -.1319923    .0698924
       _cons |   .1044444   .0422538     2.47   0.016     .0203567    .1885322
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     878
                                                       F(  1,    79) =    0.00
                                                       Prob > F      =  0.9601
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .30809

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       lomue |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0030322   .0603748     0.05   0.960    -.1171408    .1232052
       _cons |   .1044444   .0422573     2.47   0.016     .0203333    .1885555
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1762
                                                       F(  3,   160) =    0.22
                                                       Prob > F      =  0.8821
                                                       R-squared     =  0.0020
                                                       Root MSE      =  .29373

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       lomue |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0084623    .055847    -0.15   0.880    -.1187547    .1018301
     hotline |  -.0310499   .0505809    -0.61   0.540    -.1309423    .0688424
     verdade |   .0030322   .0602004     0.05   0.960    -.1158578    .1219221
       _cons |   .1044444   .0421353     2.48   0.014     .0212314    .1876575
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.22
            Prob > F =    0.8821
.88210804

Linear regression                                      Number of obs =     586
                                                       F(  1,    81) =    0.01
                                                       Prob > F      =  0.9217
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .31874

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       lomue |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0065359   .0662709    -0.10   0.922    -.1383943    .1253224
       _cons |   .1176471   .0511636     2.30   0.024     .0158476    .2194466
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    0.68
                                                       Prob > F      =  0.4111
                                                       R-squared     =  0.0066
                                                       Root MSE      =  .29095

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       lomue |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0471773   .0570987    -0.83   0.411    -.1608073    .0664527
       _cons |   .1176471   .0511674     2.30   0.024     .0158206    .2194735
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  1,    79) =    0.00
                                                       Prob > F      =  0.9880
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .32337

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       lomue |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |    .001058   .0699814     0.02   0.988    -.1382365    .1403525
       _cons |   .1176471    .051173     2.30   0.024     .0157898    .2195043
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1162
                                                       F(  3,   160) =    0.50
                                                       Prob > F      =  0.6829
                                                       R-squared     =  0.0043
                                                       Root MSE      =   .3053

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       lomue |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0065359   .0661001    -0.10   0.921    -.1370771    .1240052
     hotline |  -.0471773   .0569472    -0.83   0.409    -.1596424    .0652879
     verdade |    .001058   .0697882     0.02   0.988    -.1367669    .1388828
       _cons |   .1176471   .0510317     2.31   0.022     .0168645    .2184296
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.50
            Prob > F =    0.6829
.68294861


note: results saved to balance.xml

Linear regression                                      Number of obs =     898
                                                       F(  1,    81) =    0.05
                                                       Prob > F      =  0.8181
                                                       R-squared     =  0.0004
                                                       Root MSE      =  .29909

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      chuabo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0115774   .0501891     0.23   0.818    -.0882831    .1114379
       _cons |   .0933333    .037904     2.46   0.016     .0179162    .1687505
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     886
                                                       F(  1,    80) =    0.00
                                                       Prob > F      =  0.9750
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .29013

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      chuabo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0015902   .0505859    -0.03   0.975    -.1022594     .099079
       _cons |   .0933333   .0379072     2.46   0.016     .0178956    .1687711
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     878
                                                       F(  1,    79) =    0.00
                                                       Prob > F      =  0.9982
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .29132

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      chuabo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0001246   .0545261     0.00   0.998    -.1084068     .108656
       _cons |   .0933333   .0379104     2.46   0.016     .0178746     .168792
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1762
                                                       F(  3,   160) =    0.03
                                                       Prob > F      =  0.9919
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .29476

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      chuabo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0115774   .0500525     0.23   0.817    -.0872714    .1104261
     hotline |  -.0015902    .050444    -0.03   0.975    -.1012122    .0980318
     verdade |   .0001246   .0543686     0.00   0.998     -.107248    .1074972
       _cons |   .0933333   .0378009     2.47   0.015     .0186803    .1679864
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.03
            Prob > F =    0.9919
.99193043

Linear regression                                      Number of obs =     586
                                                       F(  1,    81) =    0.00
                                                       Prob > F      =  0.9919
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .30591

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      chuabo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0005709   .0563975     0.01   0.992    -.1116424    .1127842
       _cons |   .1038062    .041345     2.51   0.014     .0215427    .1860697
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    0.19
                                                       Prob > F      =  0.6602
                                                       R-squared     =  0.0016
                                                       Root MSE      =  .28928

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      chuabo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0232693   .0527274    -0.44   0.660    -.1282002    .0816616
       _cons |   .1038062    .041348     2.51   0.014      .021521    .1860915
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  1,    79) =    0.06
                                                       Prob > F      =  0.8130
                                                       R-squared     =  0.0005
                                                       Root MSE      =   .2964

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      chuabo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0138782   .0584654    -0.24   0.813    -.1302507    .1024944
       _cons |   .1038062   .0413525     2.51   0.014     .0214961    .1861164
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1162
                                                       F(  3,   160) =    0.10
                                                       Prob > F      =  0.9583
                                                       R-squared     =  0.0012
                                                       Root MSE      =  .29308

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      chuabo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0005709   .0562521     0.01   0.992    -.1105215    .1116632
     hotline |  -.0232693   .0525875    -0.44   0.659    -.1271245    .0805859
     verdade |  -.0138782    .058304    -0.24   0.812    -.1290229    .1012665
       _cons |   .1038062   .0412384     2.52   0.013     .0223645    .1852479
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.10
            Prob > F =    0.9583
.95831715


note: results saved to balance.xml

Linear regression                                      Number of obs =     898
                                                       F(  1,    81) =    0.67
                                                       Prob > F      =  0.4155
                                                       R-squared     =  0.0024
                                                       Root MSE      =  .22491

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    chironga |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0220337   .0269232    -0.82   0.416    -.0756025     .031535
       _cons |   .0644444   .0223921     2.88   0.005     .0198912    .1089976
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     886
                                                       F(  1,    80) =    0.60
                                                       Prob > F      =  0.4403
                                                       R-squared     =  0.0021
                                                       Root MSE      =  .22638

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    chironga |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0208665    .026908    -0.78   0.440    -.0744151    .0326822
       _cons |   .0644444   .0223939     2.88   0.005     .0198791    .1090098
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     878
                                                       F(  1,    79) =    0.27
                                                       Prob > F      =  0.6025
                                                       R-squared     =  0.0011
                                                       Root MSE      =  .23188

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    chironga |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   -.015379   .0294094    -0.52   0.602     -.073917     .043159
       _cons |   .0644444   .0223958     2.88   0.005     .0198667    .1090222
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1762
                                                       F(  3,   160) =    0.26
                                                       Prob > F      =  0.8569
                                                       R-squared     =  0.0017
                                                       Root MSE      =   .2179

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    chironga |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0220337   .0268499    -0.82   0.413    -.0750597    .0309923
     hotline |  -.0208665   .0268325    -0.78   0.438    -.0738581    .0321251
     verdade |   -.015379   .0293245    -0.52   0.601     -.073292     .042534
       _cons |   .0644444   .0223311     2.89   0.004     .0203426    .1085462
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.26
            Prob > F =    0.8569
.85692696

Linear regression                                      Number of obs =     586
                                                       F(  1,    81) =    0.55
                                                       Prob > F      =  0.4608
                                                       R-squared     =  0.0018
                                                       Root MSE      =  .21706

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    chironga |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0184195   .0248571    -0.74   0.461    -.0678773    .0310383
       _cons |   .0588235   .0182687     3.22   0.002     .0224745    .0951726
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    0.63
                                                       Prob > F      =  0.4284
                                                       R-squared     =  0.0018
                                                       Root MSE      =  .21688

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    chironga |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0185551    .023311    -0.80   0.428    -.0649454    .0278353
       _cons |   .0588235   .0182701     3.22   0.002     .0224648    .0951822
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  1,    79) =    0.34
                                                       Prob > F      =  0.5638
                                                       R-squared     =  0.0013
                                                       Root MSE      =  .22055

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    chironga |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0156581   .0270164    -0.58   0.564    -.0694329    .0381167
       _cons |   .0588235   .0182721     3.22   0.002     .0224538    .0951932
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1162
                                                       F(  3,   160) =    0.26
                                                       Prob > F      =  0.8575
                                                       R-squared     =  0.0014
                                                       Root MSE      =  .20886

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    chironga |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0184195    .024793    -0.74   0.459    -.0673832    .0305442
     hotline |  -.0185551   .0232491    -0.80   0.426    -.0644699    .0273597
     verdade |  -.0156581   .0269418    -0.58   0.562    -.0688655    .0375494
       _cons |   .0588235   .0182216     3.23   0.002     .0228376    .0948095
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.26
            Prob > F =    0.8575
.85747501


note: results saved to balance.xml

Linear regression                                      Number of obs =     898
                                                       F(  1,    81) =    0.07
                                                       Prob > F      =  0.7879
                                                       R-squared     =  0.0005
                                                       Root MSE      =  .20648

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     maconde |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0091071   .0337425     0.27   0.788    -.0580299    .0762442
       _cons |        .04    .020278     1.97   0.052    -.0003468    .0803468
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     886
                                                       F(  1,    80) =    0.01
                                                       Prob > F      =  0.9202
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .19231

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     maconde |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0033028   .0328758    -0.10   0.920    -.0687277    .0621222
       _cons |        .04   .0202797     1.97   0.052    -.0003578    .0803578
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     878
                                                       F(  1,    79) =    0.01
                                                       Prob > F      =  0.9263
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .19315

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     maconde |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0026168   .0281971    -0.09   0.926    -.0587417     .053508
       _cons |        .04   .0202814     1.97   0.052     -.000369     .080369
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1762
                                                       F(  3,   160) =    0.05
                                                       Prob > F      =  0.9853
                                                       R-squared     =  0.0006
                                                       Root MSE      =  .19813

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     maconde |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0091071   .0336507     0.27   0.787    -.0573497     .075564
     hotline |  -.0033028   .0327836    -0.10   0.920    -.0680471    .0614416
     verdade |  -.0026168   .0281156    -0.09   0.926    -.0581424    .0529088
       _cons |        .04   .0202228     1.98   0.050      .000062     .079938
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.05
            Prob > F =    0.9853
.98532583

Linear regression                                      Number of obs =     586
                                                       F(  1,    81) =    0.76
                                                       Prob > F      =  0.3859
                                                       R-squared     =  0.0053
                                                       Root MSE      =  .18139

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     maconde |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0263768   .0302543     0.87   0.386    -.0338198    .0865734
       _cons |   .0207612   .0126778     1.64   0.105    -.0044636    .0459861
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    0.25
                                                       Prob > F      =  0.6218
                                                       R-squared     =  0.0023
                                                       Root MSE      =  .16779

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     maconde |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0161515   .0326168     0.50   0.622    -.0487579    .0810609
       _cons |   .0207612   .0126787     1.64   0.105    -.0044702    .0459927
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  1,    79) =    0.55
                                                       Prob > F      =  0.4619
                                                       R-squared     =  0.0021
                                                       Root MSE      =  .16571

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     maconde |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |     .01521   .0205744     0.74   0.462    -.0257423    .0561623
       _cons |   .0207612   .0126801     1.64   0.106    -.0044779    .0460004
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1162
                                                       F(  3,   160) =    0.37
                                                       Prob > F      =  0.7740
                                                       R-squared     =  0.0026
                                                       Root MSE      =  .18457

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     maconde |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0263768   .0301763     0.87   0.383    -.0332185    .0859721
     hotline |   .0161515   .0325302     0.50   0.620    -.0480925    .0803955
     verdade |     .01521   .0205176     0.74   0.460    -.0253102    .0557302
       _cons |   .0207612   .0126451     1.64   0.103    -.0042116    .0457341
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.37
            Prob > F =    0.7740
.77396438


note: results saved to balance.xml

. 
. foreach i in $demo3 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0, cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & drops2==0, cluster(ea)
 14.         estimates store `i'_10
 15.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & drops2==0, cluster(ea)
 16.         estimates store `i'_11
 17.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & drops2==0, cluster(ea)
 18.         estimates store `i'_12
 19.         regress `i' civiceduc hotline verdade if time==0 & drops2==0, cluster(ea)
 20.         test civiceduc hotline verdade
 21.         scalar define f`i'_4=r(p)
 22.         display f`i'_4
 23. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3" + " `i'_10" + " `i'_11" + " `i'
> _12"
 24.         xml_tab $list1, below save(balance.xml) append sheet("bal `i'")
 25.         estimates clear
 26. 
. }

Linear regression                                      Number of obs =     901
                                                       F(  1,    81) =    1.02
                                                       Prob > F      =  0.3150
                                                       R-squared     =  0.0028
                                                       Root MSE      =  .48345

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cathol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0507913   .0502377    -1.01   0.315    -.1507486    .0491659
       _cons |   .3982301   .0355783    11.19   0.000     .3274405    .4690197
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     888
                                                       F(  1,    80) =    0.96
                                                       Prob > F      =  0.3310
                                                       R-squared     =  0.0031
                                                       Root MSE      =  .48302

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cathol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0541934   .0554056    -0.98   0.331     -.164454    .0560672
       _cons |   .3982301   .0355813    11.19   0.000     .3274211    .4690391
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     881
                                                       F(  1,    79) =    0.85
                                                       Prob > F      =  0.3605
                                                       R-squared     =  0.0023
                                                       Root MSE      =   .4843

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cathol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0462487   .0502824    -0.92   0.360    -.1463333    .0538359
       _cons |   .3982301   .0355842    11.19   0.000     .3274015    .4690587
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1766
                                                       F(  3,   160) =    0.49
                                                       Prob > F      =  0.6893
                                                       R-squared     =  0.0021
                                                       Root MSE      =  .48024

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cathol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0507913    .050101    -1.01   0.312    -.1497358    .0481531
     hotline |  -.0541934   .0552502    -0.98   0.328     -.163307    .0549202
     verdade |  -.0462487   .0501372    -0.92   0.358    -.1452647    .0527673
       _cons |   .3982301   .0354815    11.22   0.000     .3281577    .4683025
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.49
            Prob > F =    0.6893
.68930727

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    0.11
                                                       Prob > F      =  0.7431
                                                       R-squared     =  0.0004
                                                       Root MSE      =  .49036

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cathol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0190428   .0579134    -0.33   0.743    -.1342722    .0961867
       _cons |   .4083045   .0417486     9.78   0.000     .3252379     .491371
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    1.82
                                                       Prob > F      =  0.1814
                                                       R-squared     =  0.0074
                                                       Root MSE      =  .48082

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cathol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0828011   .0614196    -1.35   0.181      -.20503    .0394277
       _cons |   .4083045   .0417517     9.78   0.000     .3252159    .4913931
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     568
                                                       F(  1,    79) =    1.94
                                                       Prob > F      =  0.1674
                                                       R-squared     =  0.0066
                                                       Root MSE      =  .48198

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cathol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0785554   .0563801    -1.39   0.167    -.1907771    .0336663
       _cons |   .4083045   .0417562     9.78   0.000     .3251909    .4914181
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1164
                                                       F(  3,   160) =    1.03
                                                       Prob > F      =  0.3817
                                                       R-squared     =  0.0057
                                                       Root MSE      =  .48044

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cathol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0190428    .057764    -0.33   0.742     -.133121    .0950355
     hotline |  -.0828011   .0612565    -1.35   0.178    -.2037767    .0381744
     verdade |  -.0785554   .0562244    -1.40   0.164     -.189593    .0324822
       _cons |   .4083045   .0416409     9.81   0.000     .3260678    .4905412
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.03
            Prob > F =    0.3817
.38167292


note: results saved to balance.xml

Linear regression                                      Number of obs =     901
                                                       F(  1,    81) =    0.29
                                                       Prob > F      =  0.5885
                                                       R-squared     =  0.0012
                                                       Root MSE      =  .47947

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     protest |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0334568   .0616001     0.54   0.589    -.0891081    .1560218
       _cons |    .340708   .0440703     7.73   0.000     .2530219    .4283941
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     888
                                                       F(  1,    80) =    0.01
                                                       Prob > F      =  0.9312
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .47539

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     protest |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0056223   .0648987     0.09   0.931    -.1235303    .1347749
       _cons |    .340708    .044074     7.73   0.000     .2529979    .4284181
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     881
                                                       F(  1,    79) =    0.01
                                                       Prob > F      =  0.9149
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .47555

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     protest |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0066114    .061704     0.11   0.915    -.1162074    .1294301
       _cons |    .340708   .0440777     7.73   0.000     .2529736    .4284424
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1766
                                                       F(  3,   160) =    0.12
                                                       Prob > F      =  0.9486
                                                       R-squared     =  0.0007
                                                       Root MSE      =  .47802

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     protest |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0334568   .0614325     0.54   0.587    -.0878663      .15478
     hotline |   .0056223   .0647167     0.09   0.931    -.1221867    .1334313
     verdade |   .0066114   .0615258     0.11   0.915     -.114896    .1281188
       _cons |    .340708   .0439504     7.75   0.000     .2539103    .4275056
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.12
            Prob > F =    0.9486
.94861387

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    0.19
                                                       Prob > F      =  0.6671
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .47161

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     protest |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0272985   .0632251     0.43   0.667    -.0984996    .1530966
       _cons |   .3183391   .0426352     7.47   0.000     .2335083    .4031699
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    0.41
                                                       Prob > F      =  0.5229
                                                       R-squared     =  0.0022
                                                       Root MSE      =  .47425

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     protest |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |    .044077   .0686834     0.64   0.523    -.0926072    .1807613
       _cons |   .3183391   .0426385     7.47   0.000     .2334858    .4031924
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     568
                                                       F(  1,    79) =    0.33
                                                       Prob > F      =  0.5666
                                                       R-squared     =  0.0015
                                                       Root MSE      =  .47291

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     protest |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0364996   .0634185     0.58   0.567    -.0897318     .162731
       _cons |   .3183391   .0426431     7.47   0.000     .2334602     .403218
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1164
                                                       F(  3,   160) =    0.18
                                                       Prob > F      =  0.9112
                                                       R-squared     =  0.0012
                                                       Root MSE      =  .47601

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     protest |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0272985    .063062     0.43   0.666    -.0972429    .1518398
     hotline |    .044077    .068501     0.64   0.521    -.0912057    .1793598
     verdade |   .0364996   .0632434     0.58   0.565    -.0883998     .161399
       _cons |   .3183391   .0425253     7.49   0.000     .2343558    .4023224
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.18
            Prob > F =    0.9112
.91115794


note: results saved to balance.xml

Linear regression                                      Number of obs =     901
                                                       F(  1,    81) =    0.00
                                                       Prob > F      =  0.9889
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .40439

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      muslim |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0008524   .0608296    -0.01   0.989    -.1218842    .1201794
       _cons |   .2057522   .0413083     4.98   0.000     .1235616    .2879428
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     888
                                                       F(  1,    80) =    0.27
                                                       Prob > F      =  0.6061
                                                       R-squared     =  0.0018
                                                       Root MSE      =  .41634

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      muslim |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0350735   .0677579     0.52   0.606    -.0997691     .169916
       _cons |   .2057522   .0413118     4.98   0.000     .1235391    .2879653
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     881
                                                       F(  1,    79) =    0.22
                                                       Prob > F      =  0.6377
                                                       R-squared     =  0.0015
                                                       Root MSE      =  .41531

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      muslim |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |     .03201   .0677143     0.47   0.638    -.1027719     .166792
       _cons |   .2057522   .0413152     4.98   0.000     .1235163    .2879881
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1766
                                                       F(  3,   160) =    0.16
                                                       Prob > F      =  0.9204
                                                       R-squared     =  0.0017
                                                       Root MSE      =  .41569

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      muslim |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0008524    .060664    -0.01   0.989     -.120658    .1189531
     hotline |   .0350735   .0675678     0.52   0.604    -.0983663    .1685133
     verdade |     .03201   .0675188     0.47   0.636    -.1013329    .1653529
       _cons |   .2057522   .0411959     4.99   0.000     .1243944    .2871101
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.16
            Prob > F =    0.9204
.92037767

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    0.04
                                                       Prob > F      =  0.8378
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .40392

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      muslim |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0130861   .0637381    -0.21   0.838    -.1399049    .1137327
       _cons |   .2110727   .0435183     4.85   0.000     .1244848    .2976605
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    0.22
                                                       Prob > F      =  0.6384
                                                       R-squared     =  0.0016
                                                       Root MSE      =   .4201

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      muslim |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0338938   .0718416     0.47   0.638    -.1090755    .1768631
       _cons |   .2110727   .0435217     4.85   0.000     .1244618    .2976835
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     568
                                                       F(  1,    79) =    0.32
                                                       Prob > F      =  0.5743
                                                       R-squared     =  0.0022
                                                       Root MSE      =  .42151

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      muslim |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0398234   .0706077     0.56   0.574    -.1007177    .1803645
       _cons |   .2110727   .0435263     4.85   0.000     .1244357    .2977096
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1164
                                                       F(  3,   160) =    0.25
                                                       Prob > F      =  0.8578
                                                       R-squared     =  0.0028
                                                       Root MSE      =  .41833

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      muslim |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0130861   .0635737    -0.21   0.837    -.1386379    .1124657
     hotline |   .0338938   .0716508     0.47   0.637    -.1076096    .1753971
     verdade |   .0398234   .0704127     0.57   0.572    -.0992348    .1788815
       _cons |   .2110727   .0434061     4.86   0.000     .1253499    .2967955
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.25
            Prob > F =    0.8578
.85781443


note: results saved to balance.xml

. 
. foreach i in $demo4 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0, cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & drops2==0, cluster(ea)
 14.         estimates store `i'_10
 15.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & drops2==0, cluster(ea)
 16.         estimates store `i'_11
 17.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & drops2==0, cluster(ea)
 18.         estimates store `i'_12
 19.         regress `i' civiceduc hotline verdade if time==0 & drops2==0, cluster(ea)
 20.         test civiceduc hotline verdade
 21.         scalar define f`i'_4=r(p)
 22.         display f`i'_4
 23. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3" + " `i'_10" + " `i'_11" + " `i'
> _12"
 24.         xml_tab $list1, below save(balance.xml) append sheet("bal `i'")
 25.         estimates clear
 26. 
. }

Linear regression                                      Number of obs =     901
                                                       F(  1,    81) =    0.97
                                                       Prob > F      =  0.3267
                                                       R-squared     =  0.0018
                                                       Root MSE      =  .42769

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         job |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0361324    .036615    -0.99   0.327    -.1089847    .0367199
       _cons |   .2588496   .0244468    10.59   0.000     .2102081     .307491
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     888
                                                       F(  1,    80) =    0.12
                                                       Prob > F      =  0.7298
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .44218

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         job |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0140862   .0406369     0.35   0.730    -.0667837    .0949562
       _cons |   .2588496   .0244488    10.59   0.000     .2101948    .3075043
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     881
                                                       F(  1,    79) =    0.04
                                                       Prob > F      =  0.8382
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .43656

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         job |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0071013   .0346703    -0.20   0.838    -.0761108    .0619082
       _cons |   .2588496   .0244509    10.59   0.000     .2101813    .3075178
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1766
                                                       F(  3,   160) =    0.55
                                                       Prob > F      =  0.6500
                                                       R-squared     =  0.0018
                                                       Root MSE      =  .43393

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         job |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0361324   .0365153    -0.99   0.324    -.1082466    .0359818
     hotline |   .0140862   .0405229     0.35   0.729    -.0659424    .0941149
     verdade |  -.0071013   .0345702    -0.21   0.838     -.075374    .0611714
       _cons |   .2588496   .0243802    10.62   0.000      .210701    .3069981
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.55
            Prob > F =    0.6500
.65000282

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    1.38
                                                       Prob > F      =  0.2443
                                                       R-squared     =  0.0033
                                                       Root MSE      =  .41976

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         job |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0478972   .0408386    -1.17   0.244    -.1291532    .0333588
       _cons |   .2525952   .0288821     8.75   0.000     .1951289    .3100614
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    0.00
                                                       Prob > F      =  0.9843
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .43498

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         job |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0009173   .0464739    -0.02   0.984    -.0934033    .0915687
       _cons |   .2525952   .0288843     8.75   0.000     .1951136    .3100767
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     568
                                                       F(  1,    79) =    0.49
                                                       Prob > F      =  0.4871
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .42728

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         job |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0267887   .0383722    -0.70   0.487    -.1031666    .0495892
       _cons |   .2525952   .0288873     8.74   0.000     .1950963     .310094
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1164
                                                       F(  3,   160) =    0.59
                                                       Prob > F      =  0.6241
                                                       R-squared     =  0.0022
                                                       Root MSE      =  .42342

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         job |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0478972   .0407333    -1.18   0.241    -.1283414    .0325471
     hotline |  -.0009173   .0463505    -0.02   0.984     -.092455    .0906204
     verdade |  -.0267887   .0382662    -0.70   0.485    -.1023607    .0487833
       _cons |   .2525952   .0288076     8.77   0.000      .195703    .3094873
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.59
            Prob > F =    0.6241
.62410307


note: results saved to balance.xml

Linear regression                                      Number of obs =     901
                                                       F(  1,    81) =    0.16
                                                       Prob > F      =  0.6909
                                                       R-squared     =  0.0005
                                                       Root MSE      =  .47863

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       agric |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0223358   .0559632     0.40   0.691    -.0890134    .1336849
       _cons |   .3429204   .0420459     8.16   0.000     .2592621    .4265786
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     888
                                                       F(  1,    80) =    0.16
                                                       Prob > F      =  0.6893
                                                       R-squared     =  0.0007
                                                       Root MSE      =  .47098

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       agric |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   -.024113   .0600976    -0.40   0.689    -.1437111    .0954851
       _cons |   .3429204   .0420495     8.16   0.000     .2592392    .4266015
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     881
                                                       F(  1,    79) =    0.36
                                                       Prob > F      =  0.5485
                                                       R-squared     =  0.0014
                                                       Root MSE      =  .46886

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       agric |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   -.035228   .0584567    -0.60   0.548    -.1515832    .0811271
       _cons |   .3429204    .042053     8.15   0.000      .259216    .4266247
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1766
                                                       F(  3,   160) =    0.44
                                                       Prob > F      =  0.7276
                                                       R-squared     =  0.0022
                                                       Root MSE      =  .47168

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       agric |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0223358   .0558109     0.40   0.690    -.0878852    .1325568
     hotline |   -.024113    .059929    -0.40   0.688     -.142467    .0942409
     verdade |   -.035228   .0582879    -0.60   0.546    -.1503409    .0798848
       _cons |   .3429204   .0419315     8.18   0.000     .2601097     .425731
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.44
            Prob > F =    0.7276
.72760032

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    0.15
                                                       Prob > F      =  0.7041
                                                       R-squared     =  0.0006
                                                       Root MSE      =  .47794

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       agric |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0233158   .0611826     0.38   0.704    -.0984183    .1450499
       _cons |   .3391003   .0471806     7.19   0.000     .2452257    .4329749
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    0.00
                                                       Prob > F      =  0.9587
                                                       R-squared     =  0.0000
                                                       Root MSE      =   .4736

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       agric |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0035299   .0679279    -0.05   0.959    -.1387106    .1316509
       _cons |   .3391003   .0471842     7.19   0.000     .2452008    .4329999
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     568
                                                       F(  1,    79) =    0.19
                                                       Prob > F      =  0.6670
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .46927

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       agric |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0272724   .0631391    -0.43   0.667    -.1529477    .0984029
       _cons |   .3391003   .0471892     7.19   0.000     .2451725    .4330282
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1164
                                                       F(  3,   160) =    0.26
                                                       Prob > F      =  0.8519
                                                       R-squared     =  0.0014
                                                       Root MSE      =  .47338

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       agric |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0233158   .0610248     0.38   0.703    -.0972022    .1438337
     hotline |  -.0035299   .0677475    -0.05   0.959    -.1373246    .1302648
     verdade |  -.0272724   .0629648    -0.43   0.665    -.1516216    .0970768
       _cons |   .3391003   .0470589     7.21   0.000     .2461636    .4320371
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.26
            Prob > F =    0.8519
.85193821


note: results saved to balance.xml

Linear regression                                      Number of obs =     899
                                                       F(  1,    81) =    0.60
                                                       Prob > F      =  0.4409
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .19357

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         com |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0112101    .014473     0.77   0.441    -.0175867    .0400069
       _cons |   .0333333   .0093575     3.56   0.001     .0147149    .0519517
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     885
                                                       F(  1,    80) =    1.22
                                                       Prob > F      =  0.2728
                                                       R-squared     =  0.0014
                                                       Root MSE      =  .19763

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         com |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0149425   .0135326     1.10   0.273    -.0119881    .0418732
       _cons |   .0333333   .0093583     3.56   0.001     .0147098    .0519568
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     876
                                                       F(  1,    79) =    0.02
                                                       Prob > F      =  0.8898
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .18207

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         com |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0018779   .0135151     0.14   0.890    -.0250232     .028779
       _cons |   .0333333    .009359     3.56   0.001     .0147046     .051962
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1760
                                                       F(  3,   160) =    0.55
                                                       Prob > F      =  0.6502
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .19688

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         com |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0112101   .0144337     0.78   0.439     -.017295    .0397152
     hotline |   .0149425   .0134946     1.11   0.270     -.011708     .041593
     verdade |   .0018779    .013476     0.14   0.889    -.0247359    .0284918
       _cons |   .0333333    .009332     3.57   0.000     .0149035    .0517631
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.55
            Prob > F =    0.6502
.65022587

Linear regression                                      Number of obs =     586
                                                       F(  1,    81) =    0.01
                                                       Prob > F      =  0.9324
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .20626

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         com |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0015147   .0178024    -0.09   0.932    -.0369359    .0339064
       _cons |   .0451389   .0133917     3.37   0.001     .0184935    .0717842
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     586
                                                       F(  1,    80) =    0.08
                                                       Prob > F      =  0.7831
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .21365

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         com |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0051967    .018818     0.28   0.783    -.0322524    .0426458
       _cons |   .0451389   .0133928     3.37   0.001     .0184864    .0717913
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  1,    79) =    0.08
                                                       Prob > F      =  0.7749
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .20184

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         com |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0055705   .0194107    -0.29   0.775    -.0442066    .0330656
       _cons |   .0451389   .0133942     3.37   0.001     .0184784    .0717994
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1162
                                                       F(  3,   160) =    0.11
                                                       Prob > F      =  0.9549
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .20708

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         com |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0015147   .0177565    -0.09   0.932     -.036582    .0335525
     hotline |   .0051967   .0187681     0.28   0.782    -.0318684    .0422618
     verdade |  -.0055705   .0193571    -0.29   0.774    -.0437989    .0326578
       _cons |   .0451389   .0133572     3.38   0.001     .0187597    .0715181
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.11
            Prob > F =    0.9549
.95486154


note: results saved to balance.xml

Linear regression                                      Number of obs =     899
                                                       F(  1,    81) =    2.04
                                                       Prob > F      =  0.1573
                                                       R-squared     =  0.0023
                                                       Root MSE      =  .18527

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         art |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0177184   .0124119    -1.43   0.157    -.0424142    .0069774
       _cons |   .0444444   .0095477     4.65   0.000     .0254474    .0634414
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     885
                                                       F(  1,    80) =    0.08
                                                       Prob > F      =  0.7722
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .21042

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         art |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0038314   .0131888     0.29   0.772    -.0224152     .030078
       _cons |   .0444444   .0095485     4.65   0.000     .0254422    .0634467
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     876
                                                       F(  1,    79) =    0.12
                                                       Prob > F      =  0.7338
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .21145

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         art |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0048513   .0142146     0.34   0.734    -.0234422    .0331448
       _cons |   .0444444   .0095493     4.65   0.000     .0254369    .0634519
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1760
                                                       F(  3,   160) =    1.55
                                                       Prob > F      =  0.2045
                                                       R-squared     =  0.0021
                                                       Root MSE      =  .20071

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         art |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0177184   .0123781    -1.43   0.154     -.042164    .0067272
     hotline |   .0038314   .0131518     0.29   0.771    -.0221422     .029805
     verdade |   .0048513   .0141736     0.34   0.733    -.0231401    .0328428
       _cons |   .0444444   .0095218     4.67   0.000     .0256399     .063249
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.55
            Prob > F =    0.2045
.20448088

Linear regression                                      Number of obs =     586
                                                       F(  1,    81) =    4.62
                                                       Prob > F      =  0.0346
                                                       R-squared     =  0.0081
                                                       Root MSE      =  .17671

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         art |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0318326   .0148115    -2.15   0.035    -.0613027   -.0023624
       _cons |   .0486111   .0129692     3.75   0.000     .0228064    .0744158
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     586
                                                       F(  1,    80) =    0.01
                                                       Prob > F      =  0.9245
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .21367

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         art |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0016312   .0171645    -0.10   0.925    -.0357897    .0325272
       _cons |   .0486111   .0129702     3.75   0.000     .0227996    .0744226
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  1,    79) =    0.40
                                                       Prob > F      =  0.5277
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .22785

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         art |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |     .01254   .0197695     0.63   0.528    -.0268103    .0518903
       _cons |   .0486111   .0129716     3.75   0.000     .0227918    .0744304
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1162
                                                       F(  3,   160) =    3.92
                                                       Prob > F      =  0.0098
                                                       R-squared     =  0.0065
                                                       Root MSE      =  .20261

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         art |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0318326   .0147733    -2.15   0.033    -.0610083   -.0026568
     hotline |  -.0016312    .017119    -0.10   0.924    -.0354395     .032177
     verdade |     .01254   .0197149     0.64   0.526    -.0263951     .051475
       _cons |   .0486111   .0129358     3.76   0.000     .0230642     .074158
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    3.92
            Prob > F =    0.0098
.00984692


note: results saved to balance.xml

Linear regression                                      Number of obs =     899
                                                       F(  1,    81) =    0.40
                                                       Prob > F      =  0.5265
                                                       R-squared     =  0.0006
                                                       Root MSE      =  .21823

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         man |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0110121   .0173122    -0.64   0.527    -.0454581    .0234338
       _cons |   .0555556   .0125756     4.42   0.000      .030534    .0805771
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     885
                                                       F(  1,    80) =    0.79
                                                       Prob > F      =  0.3772
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .24361

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         man |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0157088   .0176903     0.89   0.377     -.019496    .0509136
       _cons |   .0555556   .0125767     4.42   0.000     .0305272    .0805839
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     876
                                                       F(  1,    79) =    0.52
                                                       Prob > F      =  0.4710
                                                       R-squared     =  0.0007
                                                       Root MSE      =   .2407

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         man |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0125196   .0172851     0.72   0.471    -.0218856    .0469247
       _cons |   .0555556   .0125777     4.42   0.000     .0305202    .0805909
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1760
                                                       F(  3,   160) =    1.04
                                                       Prob > F      =  0.3778
                                                       R-squared     =  0.0020
                                                       Root MSE      =  .23688

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         man |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0110121   .0172652    -0.64   0.525    -.0451091    .0230849
     hotline |   .0157088   .0176407     0.89   0.375    -.0191298    .0505474
     verdade |   .0125196   .0172352     0.73   0.469    -.0215183    .0465574
       _cons |   .0555556   .0125414     4.43   0.000     .0307875    .0803236
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.04
            Prob > F =    0.3778
.37777153

Linear regression                                      Number of obs =     586
                                                       F(  1,    81) =    0.44
                                                       Prob > F      =  0.5107
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .20992

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         man |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0118149   .0178813    -0.66   0.511    -.0473931    .0237633
       _cons |   .0520833   .0142015     3.67   0.000     .0238268    .0803398
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     586
                                                       F(  1,    80) =    0.07
                                                       Prob > F      =  0.7986
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .22759

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         man |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0049636   .0193911     0.26   0.799    -.0336259    .0435532
       _cons |   .0520833   .0142026     3.67   0.000     .0238193    .0803474
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  1,    79) =    0.41
                                                       Prob > F      =  0.5246
                                                       R-squared     =  0.0007
                                                       Root MSE      =  .23465

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         man |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0126649   .0198146     0.64   0.525     -.026775    .0521048
       _cons |   .0520833   .0142041     3.67   0.000     .0238107    .0803559
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1162
                                                       F(  3,   160) =    0.72
                                                       Prob > F      =  0.5385
                                                       R-squared     =  0.0016
                                                       Root MSE      =  .22495

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         man |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0118149   .0178352    -0.66   0.509    -.0470377    .0234079
     hotline |   .0049636   .0193397     0.26   0.798    -.0332303    .0431576
     verdade |   .0126649   .0197598     0.64   0.522    -.0263588    .0516886
       _cons |   .0520833   .0141649     3.68   0.000     .0241091    .0800576
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.72
            Prob > F =    0.5385
.53854969


note: results saved to balance.xml

Linear regression                                      Number of obs =     899
                                                       F(  1,    81) =    1.05
                                                       Prob > F      =  0.3097
                                                       R-squared     =  0.0013
                                                       Root MSE      =  .15111

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       assal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0110715   .0108301    -1.02   0.310    -.0326199    .0104769
       _cons |   .0288889   .0092189     3.13   0.002     .0105463    .0472315
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     885
                                                       F(  1,    80) =    0.07
                                                       Prob > F      =  0.7922
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .17217

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       assal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |    .003295   .0124623     0.26   0.792    -.0215057    .0280958
       _cons |   .0288889   .0092196     3.13   0.002     .0105412    .0472365
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     876
                                                       F(  1,    79) =    0.00
                                                       Prob > F      =  0.9554
                                                       R-squared     =  0.0000
                                                       Root MSE      =   .1667

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       assal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0007199    .012833    -0.06   0.955    -.0262633    .0248235
       _cons |   .0288889   .0092204     3.13   0.002     .0105361    .0472417
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1760
                                                       F(  3,   160) =    0.88
                                                       Prob > F      =  0.4542
                                                       R-squared     =  0.0011
                                                       Root MSE      =  .16131

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       assal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0110715   .0108006    -1.03   0.307    -.0324016    .0102586
     hotline |    .003295   .0124273     0.27   0.791    -.0212478    .0278378
     verdade |  -.0007199   .0127959    -0.06   0.955    -.0259906    .0245508
       _cons |   .0288889   .0091938     3.14   0.002     .0107321    .0470457
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.88
            Prob > F =    0.4542
.45415734

Linear regression                                      Number of obs =     586
                                                       F(  1,    81) =    1.03
                                                       Prob > F      =  0.3129
                                                       R-squared     =  0.0018
                                                       Root MSE      =  .17269

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       assal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0147045   .0144795    -1.02   0.313    -.0435141    .0141051
       _cons |   .0381944   .0119866     3.19   0.002     .0143448    .0620441
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     586
                                                       F(  1,    80) =    0.54
                                                       Prob > F      =  0.4627
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .17733

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       assal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0113488   .0153797    -0.74   0.463    -.0419554    .0192578
       _cons |   .0381944   .0119875     3.19   0.002     .0143385    .0620504
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  1,    79) =    2.96
                                                       Prob > F      =  0.0894
                                                       R-squared     =  0.0055
                                                       Root MSE      =  .16046

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       assal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   -.023806   .0138441    -1.72   0.089    -.0513619      .00375
       _cons |   .0381944   .0119888     3.19   0.002     .0143312    .0620576
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1162
                                                       F(  3,   160) =    1.11
                                                       Prob > F      =  0.3477
                                                       R-squared     =  0.0028
                                                       Root MSE      =  .15864

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       assal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0147045   .0144421    -1.02   0.310    -.0432263    .0138173
     hotline |  -.0113488   .0153389    -0.74   0.460    -.0416416     .018944
     verdade |   -.023806   .0138058    -1.72   0.087    -.0510711    .0034592
       _cons |   .0381944   .0119557     3.19   0.002     .0145831    .0618058
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.11
            Prob > F =    0.3477
.34774268


note: results saved to balance.xml

Linear regression                                      Number of obs =     899
                                                       F(  1,    81) =    0.00
                                                       Prob > F      =  0.9945
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .20642

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         tea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .000099   .0142763     0.01   0.994    -.0283064    .0285043
       _cons |   .0444444   .0105862     4.20   0.000     .0233812    .0655077
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     885
                                                       F(  1,    80) =    0.79
                                                       Prob > F      =  0.3760
                                                       R-squared     =  0.0024
                                                       Root MSE      =  .22868

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         tea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0222222   .0249621     0.89   0.376    -.0274539    .0718983
       _cons |   .0444444   .0105871     4.20   0.000     .0233754    .0655135
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     876
                                                       F(  1,    79) =    0.37
                                                       Prob > F      =  0.5433
                                                       R-squared     =  0.0005
                                                       Root MSE      =  .21624

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         tea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0095462   .0156361     0.61   0.543    -.0215767     .040669
       _cons |   .0444444    .010588     4.20   0.000     .0233695    .0655194
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1760
                                                       F(  3,   160) =    0.40
                                                       Prob > F      =  0.7538
                                                       R-squared     =  0.0017
                                                       Root MSE      =  .22264

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         tea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .000099   .0142375     0.01   0.994    -.0280186    .0282166
     hotline |   .0222222   .0248921     0.89   0.373    -.0269372    .0713816
     verdade |   .0095462   .0155909     0.61   0.541    -.0212444    .0403367
       _cons |   .0444444   .0105574     4.21   0.000     .0235945    .0652943
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.40
            Prob > F =    0.7538
.75379102

Linear regression                                      Number of obs =     586
                                                       F(  1,    81) =    0.11
                                                       Prob > F      =  0.7417
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .20243

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         tea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0048704    .014725    -0.33   0.742    -.0341685    .0244276
       _cons |   .0451389   .0110274     4.09   0.000     .0231977      .06708
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     586
                                                       F(  1,    80) =    0.21
                                                       Prob > F      =  0.6448
                                                       R-squared     =  0.0007
                                                       Root MSE      =  .22069

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         tea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0119081   .0257296     0.46   0.645    -.0392954    .0631116
       _cons |   .0451389   .0110283     4.09   0.000     .0231919    .0670859
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  1,    79) =    0.08
                                                       Prob > F      =  0.7824
                                                       R-squared     =  0.0001
                                                       Root MSE      =   .2135

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         tea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0052208   .0188413     0.28   0.782    -.0322819    .0427236
       _cons |   .0451389   .0110295     4.09   0.000     .0231853    .0670925
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1162
                                                       F(  3,   160) =    0.21
                                                       Prob > F      =  0.8894
                                                       R-squared     =  0.0009
                                                       Root MSE      =  .21445

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         tea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0048704    .014687    -0.33   0.741    -.0338758     .024135
     hotline |   .0119081   .0256613     0.46   0.643    -.0387704    .0625866
     verdade |   .0052208   .0187893     0.28   0.781    -.0318862    .0423278
       _cons |   .0451389    .010999     4.10   0.000     .0234169    .0668609
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.21
            Prob > F =    0.8894
.88940111


note: results saved to balance.xml

Linear regression                                      Number of obs =     899
                                                       F(  1,    81) =    3.01
                                                       Prob > F      =  0.0864
                                                       R-squared     =  0.0056
                                                       Root MSE      =   .1793

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      puboff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0267706   .0154217     1.74   0.086    -.0039137    .0574549
       _cons |        .02    .007426     2.69   0.009     .0052247    .0347753
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     885
                                                       F(  1,    80) =    1.09
                                                       Prob > F      =  0.2990
                                                       R-squared     =  0.0015
                                                       Root MSE      =  .15916

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      puboff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0121839   .0116546     1.05   0.299    -.0110095    .0353774
       _cons |        .02   .0074266     2.69   0.009     .0052206    .0347794
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     876
                                                       F(  1,    79) =    0.23
                                                       Prob > F      =  0.6293
                                                       R-squared     =  0.0004
                                                       Root MSE      =  .14951

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      puboff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0058216   .0120139     0.48   0.629    -.0180914    .0297346
       _cons |        .02   .0074272     2.69   0.009     .0052165    .0347835
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1760
                                                       F(  3,   160) =    1.12
                                                       Prob > F      =  0.3410
                                                       R-squared     =  0.0033
                                                       Root MSE      =   .1739

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      puboff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0267706   .0153797     1.74   0.084    -.0036028     .057144
     hotline |   .0121839   .0116219     1.05   0.296    -.0107683    .0351361
     verdade |   .0058216   .0119792     0.49   0.628    -.0178361    .0294793
       _cons |        .02   .0074058     2.70   0.008     .0053743    .0346257
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.12
            Prob > F =    0.3410
.34095525

Linear regression                                      Number of obs =     586
                                                       F(  1,    81) =    2.96
                                                       Prob > F      =  0.0893
                                                       R-squared     =  0.0068
                                                       Root MSE      =  .19785

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      puboff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0327414   .0190416     1.72   0.089    -.0051454    .0706282
       _cons |   .0243056   .0095836     2.54   0.013     .0052371     .043374
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     586
                                                       F(  1,    80) =    0.48
                                                       Prob > F      =  0.4887
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .16806

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      puboff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0092515   .0132999     0.70   0.489    -.0172161     .035719
       _cons |   .0243056   .0095844     2.54   0.013     .0052321     .043379
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  1,    79) =    0.00
                                                       Prob > F      =  0.9559
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .15559

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      puboff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0008743   .0157691     0.06   0.956    -.0305133    .0322619
       _cons |   .0243056   .0095854     2.54   0.013     .0052263    .0433848
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1162
                                                       F(  3,   160) =    1.10
                                                       Prob > F      =  0.3515
                                                       R-squared     =  0.0052
                                                       Root MSE      =  .18434

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      puboff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0327414   .0189925     1.72   0.087    -.0047669    .0702498
     hotline |   .0092515   .0132646     0.70   0.487    -.0169447    .0354477
     verdade |   .0008743   .0157255     0.06   0.956     -.030182    .0319307
       _cons |   .0243056   .0095589     2.54   0.012     .0054276    .0431835
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.10
            Prob > F =    0.3515
.35149291


note: results saved to balance.xml

Linear regression                                      Number of obs =     899
                                                       F(  1,    81) =    0.58
                                                       Prob > F      =  0.4504
                                                       R-squared     =  0.0009
                                                       Root MSE      =  .18817

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        stud |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0112051   .0147754     0.76   0.450    -.0181933    .0406036
       _cons |   .0311111   .0087234     3.57   0.001     .0137542     .048468
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     885
                                                       F(  1,    80) =    0.01
                                                       Prob > F      =  0.9319
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .17523

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        stud |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0010728   .0125077     0.09   0.932    -.0238183    .0259639
       _cons |   .0311111   .0087242     3.57   0.001     .0137495    .0484728
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     876
                                                       F(  1,    79) =    0.72
                                                       Prob > F      =  0.4001
                                                       R-squared     =  0.0009
                                                       Root MSE      =  .18774

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        stud |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0111424   .0131694     0.85   0.400    -.0150705    .0373553
       _cons |   .0311111   .0087249     3.57   0.001     .0137446    .0484776
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1760
                                                       F(  3,   160) =    0.39
                                                       Prob > F      =  0.7570
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .18873

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        stud |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0112051   .0147352     0.76   0.448    -.0178955    .0403057
     hotline |   .0010728   .0124726     0.09   0.932    -.0235594     .025705
     verdade |   .0111424   .0131313     0.85   0.397    -.0147906    .0370755
       _cons |   .0311111   .0086997     3.58   0.000       .01393    .0482922
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.39
            Prob > F =    0.7570
.75697056

Linear regression                                      Number of obs =     586
                                                       F(  1,    81) =    0.08
                                                       Prob > F      =  0.7843
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .20625

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        stud |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0053132   .0193513     0.27   0.784    -.0331898    .0438162
       _cons |   .0416667   .0124137     3.36   0.001     .0169673     .066366
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     586
                                                       F(  1,    80) =    0.43
                                                       Prob > F      =  0.5146
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .18611

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        stud |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0114653   .0175167    -0.65   0.515    -.0463247    .0233941
       _cons |   .0416667   .0124146     3.36   0.001     .0169608    .0663726
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  1,    79) =    0.12
                                                       Prob > F      =  0.7322
                                                       R-squared     =  0.0002
                                                       Root MSE      =   .1936

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        stud |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0056954   .0165839    -0.34   0.732    -.0387049     .027314
       _cons |   .0416667    .012416     3.36   0.001     .0169533      .06638
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1162
                                                       F(  3,   160) =    0.30
                                                       Prob > F      =  0.8278
                                                       R-squared     =  0.0011
                                                       Root MSE      =  .19317

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        stud |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0053132   .0193014     0.28   0.783    -.0328051    .0434315
     hotline |  -.0114653   .0174702    -0.66   0.513    -.0459673    .0230366
     verdade |  -.0056954   .0165381    -0.34   0.731    -.0383566    .0269657
       _cons |   .0416667   .0123817     3.37   0.001     .0172141    .0661193
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.30
            Prob > F =    0.8278
.82784005


note: results saved to balance.xml

Linear regression                                      Number of obs =     901
                                                       F(  1,    81) =    0.06
                                                       Prob > F      =  0.8097
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .34134

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         dom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.005765    .023862    -0.24   0.810    -.0532429    .0417128
       _cons |   .1371681   .0181272     7.57   0.000     .1011006    .1732357
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     888
                                                       F(  1,    80) =    0.79
                                                       Prob > F      =  0.3768
                                                       R-squared     =  0.0011
                                                       Root MSE      =  .33218

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         dom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0224892    .025305    -0.89   0.377    -.0728477    .0278692
       _cons |   .1371681   .0181288     7.57   0.000     .1010907    .1732456
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     881
                                                       F(  1,    79) =    0.39
                                                       Prob > F      =  0.5327
                                                       R-squared     =  0.0006
                                                       Root MSE      =  .33593

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         dom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   -.015956   .0254597    -0.63   0.533    -.0666323    .0347202
       _cons |   .1371681   .0181303     7.57   0.000     .1010807    .1732556
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1766
                                                       F(  3,   160) =    0.33
                                                       Prob > F      =  0.8043
                                                       R-squared     =  0.0007
                                                       Root MSE      =  .33242

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         dom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.005765    .023797    -0.24   0.809    -.0527619    .0412318
     hotline |  -.0224892    .025234    -0.89   0.374    -.0723238    .0273453
     verdade |   -.015956   .0253862    -0.63   0.531    -.0660912    .0341792
       _cons |   .1371681   .0180779     7.59   0.000      .101466    .1728703
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.33
            Prob > F =    0.8043
.80431671

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    0.02
                                                       Prob > F      =  0.8761
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .35421

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         dom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0044936    .028729    -0.16   0.876    -.0616553    .0526681
       _cons |   .1487889   .0207018     7.19   0.000     .1075989    .1899789
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    1.46
                                                       Prob > F      =  0.2308
                                                       R-squared     =  0.0026
                                                       Root MSE      =  .33772

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         dom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   -.034695   .0287345    -1.21   0.231    -.0918784    .0224885
       _cons |   .1487889   .0207033     7.19   0.000      .107588    .1899899
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     568
                                                       F(  1,    79) =    0.43
                                                       Prob > F      =  0.5115
                                                       R-squared     =  0.0008
                                                       Root MSE      =   .3465

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         dom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0197567   .0299583    -0.66   0.512    -.0793873    .0398739
       _cons |   .1487889   .0207055     7.19   0.000     .1075756    .1900023
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1164
                                                       F(  3,   160) =    0.61
                                                       Prob > F      =  0.6078
                                                       R-squared     =  0.0016
                                                       Root MSE      =  .34098

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         dom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0044936   .0286549    -0.16   0.876    -.0610842     .052097
     hotline |   -.034695   .0286582    -1.21   0.228    -.0912921    .0219021
     verdade |  -.0197567   .0298756    -0.66   0.509    -.0787581    .0392447
       _cons |   .1487889   .0206484     7.21   0.000     .1080104    .1895674
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.61
            Prob > F =    0.6078
.6077889


note: results saved to balance.xml

. 
. foreach i in $demo5 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0, cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & drops2==0, cluster(ea)
 14.         estimates store `i'_10
 15.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & drops2==0, cluster(ea)
 16.         estimates store `i'_11
 17.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & drops2==0, cluster(ea)
 18.         estimates store `i'_12
 19.         regress `i' civiceduc hotline verdade if time==0 & drops2==0, cluster(ea)
 20.         test civiceduc hotline verdade
 21.         scalar define f`i'_4=r(p)
 22.         display f`i'_4
 23. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3" + " `i'_10" + " `i'_11" + " `i'
> _12"
 24.         xml_tab $list1, below save(balance.xml) append sheet("bal `i'")
 25.         estimates clear
 26. 
. }

Linear regression                                      Number of obs =     900
                                                       F(  1,    81) =    0.00
                                                       Prob > F      =  0.9570
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .35964

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       house |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0015457   .0285953     0.05   0.957      -.05535    .0584413
       _cons |   .8470067   .0210661    40.21   0.000     .8050918    .8889216
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     886
                                                       F(  1,    80) =    0.02
                                                       Prob > F      =  0.8952
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .35868

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       house |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0035681   .0269996     0.13   0.895    -.0501628    .0572989
       _cons |   .8470067   .0210679    40.20   0.000     .8050802    .8889331
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     880
                                                       F(  1,    79) =    0.64
                                                       Prob > F      =  0.4266
                                                       R-squared     =  0.0009
                                                       Root MSE      =  .37021

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       house |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0218318   .0273186    -0.80   0.427    -.0762081    .0325444
       _cons |   .8470067   .0210696    40.20   0.000     .8050687    .8889446
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1764
                                                       F(  3,   160) =    0.46
                                                       Prob > F      =  0.7124
                                                       R-squared     =  0.0008
                                                       Root MSE      =   .3641

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       house |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0015457   .0285175     0.05   0.957    -.0547735    .0578649
     hotline |   .0035681   .0269238     0.13   0.895    -.0496038    .0567399
     verdade |  -.0218318   .0272397    -0.80   0.424    -.0756275    .0319639
       _cons |   .8470067   .0210088    40.32   0.000     .8055164    .8884969
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.46
            Prob > F =    0.7124
.71241179

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    0.52
                                                       Prob > F      =  0.4738
                                                       R-squared     =  0.0009
                                                       Root MSE      =  .35234

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       house |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0214812   .0298516     0.72   0.474    -.0379142    .0808765
       _cons |   .8442907   .0224618    37.59   0.000     .7995988    .8889825
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    0.73
                                                       Prob > F      =  0.3942
                                                       R-squared     =  0.0013
                                                       Root MSE      =  .35055

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       house |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0248369   .0289948     0.86   0.394    -.0328646    .0825383
       _cons |   .8442907   .0224635    37.59   0.000     .7995869    .8889944
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     568
                                                       F(  1,    79) =    0.09
                                                       Prob > F      =  0.7696
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .36742

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       house |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0091652    .031189    -0.29   0.770    -.0712454     .052915
       _cons |   .8442907   .0224659    37.58   0.000     .7995735    .8890078
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1164
                                                       F(  3,   160) =    0.66
                                                       Prob > F      =  0.5795
                                                       R-squared     =  0.0016
                                                       Root MSE      =  .35347

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       house |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0214812   .0297746     0.72   0.472    -.0373208    .0802831
     hotline |   .0248369   .0289178     0.86   0.392    -.0322729    .0819466
     verdade |  -.0091652   .0311029    -0.29   0.769    -.0705903    .0522599
       _cons |   .8442907   .0224038    37.69   0.000     .8000453     .888536
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.66
            Prob > F =    0.5795
.57951596


note: results saved to balance.xml

Linear regression                                      Number of obs =     900
                                                       F(  1,    81) =    0.03
                                                       Prob > F      =  0.8682
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .48973

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        land |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0084297   .0506434    -0.17   0.868    -.1091942    .0923348
       _cons |   .6075388   .0358424    16.95   0.000     .5362237    .6788539
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     886
                                                       F(  1,    80) =    0.14
                                                       Prob > F      =  0.7065
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .48677

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        land |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0177486   .0469665     0.38   0.707    -.0757178     .111215
       _cons |   .6075388   .0358454    16.95   0.000     .5362041    .6788735
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     880
                                                       F(  1,    79) =    0.12
                                                       Prob > F      =  0.7304
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .49061

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        land |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0177952   .0514631    -0.35   0.730      -.12023    .0846396
       _cons |   .6075388   .0358484    16.95   0.000     .5361844    .6788932
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1764
                                                       F(  3,   160) =    0.21
                                                       Prob > F      =  0.8886
                                                       R-squared     =  0.0007
                                                       Root MSE      =  .48914

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        land |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0084297   .0505056    -0.17   0.868    -.1081733    .0913139
     hotline |   .0177486   .0468348     0.38   0.705    -.0747455    .1102426
     verdade |  -.0177952   .0513145    -0.35   0.729    -.1191364     .083546
       _cons |   .6075388   .0357449    17.00   0.000     .5369462    .6781314
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.21
            Prob > F =    0.8886
.88858608

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    0.78
                                                       Prob > F      =  0.3790
                                                       R-squared     =  0.0026
                                                       Root MSE      =  .48513

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        land |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0497434   .0562303    -0.88   0.379     -.161624    .0621373
       _cons |   .6470588   .0414758    15.60   0.000     .5645351    .7295826
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    0.48
                                                       Prob > F      =  0.4885
                                                       R-squared     =  0.0017
                                                       Root MSE      =  .48404

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        land |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0396763   .0570094    -0.70   0.488    -.1531286    .0737761
       _cons |   .6470588   .0414789    15.60   0.000     .5645132    .7296045
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     568
                                                       F(  1,    79) =    0.49
                                                       Prob > F      =  0.4844
                                                       R-squared     =  0.0018
                                                       Root MSE      =  .48408

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        land |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0413241   .0588258    -0.70   0.484    -.1584138    .0757657
       _cons |   .6470588   .0414833    15.60   0.000     .5644883    .7296294
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1164
                                                       F(  3,   160) =    0.30
                                                       Prob > F      =  0.8253
                                                       R-squared     =  0.0016
                                                       Root MSE      =  .48723

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        land |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0497434   .0560853    -0.89   0.376    -.1605063    .0610195
     hotline |  -.0396763   .0568581    -0.70   0.486    -.1519654    .0726128
     verdade |  -.0413241   .0586633    -0.70   0.482    -.1571783    .0745302
       _cons |   .6470588   .0413688    15.64   0.000     .5653595    .7287581
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.30
            Prob > F =    0.8253
.82532739


note: results saved to balance.xml

Linear regression                                      Number of obs =     900
                                                       F(  1,    81) =    0.00
                                                       Prob > F      =  0.9783
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .43603

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cattle |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0010914   .0399822    -0.03   0.978    -.0806434    .0784607
       _cons |   .2549889   .0280598     9.09   0.000     .1991588    .3108191
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     886
                                                       F(  1,    80) =    0.04
                                                       Prob > F      =  0.8329
                                                       R-squared     =  0.0001
                                                       Root MSE      =   .4338

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cattle |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0090119   .0425618    -0.21   0.833    -.0937125    .0756887
       _cons |   .2549889   .0280622     9.09   0.000     .1991434    .3108344
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     880
                                                       F(  1,    79) =    0.09
                                                       Prob > F      =  0.7653
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .43983

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cattle |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0130764   .0436477     0.30   0.765    -.0738022     .099955
       _cons |   .2549889   .0280645     9.09   0.000      .199128    .3108498
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1764
                                                       F(  3,   160) =    0.08
                                                       Prob > F      =  0.9717
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .43666

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cattle |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0010914   .0398734    -0.03   0.978    -.0798375    .0776547
     hotline |  -.0090119   .0424424    -0.21   0.832    -.0928314    .0748076
     verdade |   .0130764   .0435217     0.30   0.764    -.0728747    .0990274
       _cons |   .2549889   .0279834     9.11   0.000     .1997244    .3102534
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.08
            Prob > F =    0.9717
.97172792

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    0.01
                                                       Prob > F      =  0.9225
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .43399

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cattle |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.004273   .0437801    -0.10   0.922    -.0913817    .0828357
       _cons |   .2525952   .0305297     8.27   0.000     .1918506    .3133397
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    0.03
                                                       Prob > F      =  0.8573
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .43784

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cattle |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0091498   .0507358     0.18   0.857    -.0918177    .1101173
       _cons |   .2525952    .030532     8.27   0.000     .1918345    .3133558
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     568
                                                       F(  1,    79) =    0.01
                                                       Prob > F      =  0.9086
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .43678

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cattle |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0054694   .0474705     0.12   0.909    -.0890184    .0999571
       _cons |   .2525952   .0305353     8.27   0.000     .1918162    .3133741
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1164
                                                       F(  3,   160) =    0.03
                                                       Prob > F      =  0.9936
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .43667

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cattle |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.004273   .0436672    -0.10   0.922    -.0905115    .0819655
     hotline |   .0091498   .0506011     0.18   0.857    -.0907824     .109082
     verdade |   .0054694   .0473395     0.12   0.908    -.0880214    .0989601
       _cons |   .2525952    .030451     8.30   0.000     .1924575    .3127328
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.03
            Prob > F =    0.9936
.99362996


note: results saved to balance.xml

Linear regression                                      Number of obs =     900
                                                       F(  1,    81) =    0.00
                                                       Prob > F      =  0.9826
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .45478

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         cel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0012938    .059172    -0.02   0.983    -.1190276      .11644
       _cons |   .7095344   .0405467    17.50   0.000     .6288592    .7902095
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     886
                                                       F(  1,    80) =    0.80
                                                       Prob > F      =  0.3750
                                                       R-squared     =  0.0034
                                                       Root MSE      =  .44121

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         cel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0513852   .0575953     0.89   0.375    -.0632331    .1660035
       _cons |   .7095344   .0405501    17.50   0.000     .6288371    .7902316
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     880
                                                       F(  1,    79) =    0.39
                                                       Prob > F      =  0.5349
                                                       R-squared     =  0.0015
                                                       Root MSE      =  .44612

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         cel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0340554   .0546382     0.62   0.535    -.0746992    .1428099
       _cons |   .7095344   .0405534    17.50   0.000     .6288148    .7902539
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1764
                                                       F(  3,   160) =    0.41
                                                       Prob > F      =  0.7490
                                                       R-squared     =  0.0026
                                                       Root MSE      =  .44381

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         cel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0012938    .059011    -0.02   0.983    -.1178348    .1152471
     hotline |   .0513852   .0574337     0.89   0.372    -.0620407    .1648111
     verdade |   .0340554   .0544804     0.63   0.533     -.073538    .1416488
       _cons |   .7095344   .0404363    17.55   0.000     .6296766    .7893921
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.41
            Prob > F =    0.7490
.74896677

Linear regression                                      Number of obs =     587
                                                       F(  1,    81) =    0.21
                                                       Prob > F      =  0.6515
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .45252

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         cel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .029226   .0644739     0.45   0.652    -.0990569    .1575089
       _cons |   .6989619   .0473475    14.76   0.000     .6047553    .7931685
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     587
                                                       F(  1,    80) =    0.95
                                                       Prob > F      =  0.3325
                                                       R-squared     =  0.0050
                                                       Root MSE      =  .44317

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         cel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |    .062783   .0643986     0.97   0.333    -.0653743    .1909404
       _cons |   .6989619   .0473511    14.76   0.000     .6047303    .7931936
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     568
                                                       F(  1,    79) =    0.20
                                                       Prob > F      =  0.6534
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .45292

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         cel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0286366    .063536     0.45   0.653    -.0978286    .1551019
       _cons |   .6989619   .0473561    14.76   0.000     .6047019     .793222
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1164
                                                       F(  3,   160) =    0.32
                                                       Prob > F      =  0.8088
                                                       R-squared     =  0.0025
                                                       Root MSE      =  .44448

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         cel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .029226   .0643077     0.45   0.650    -.0977753    .1562273
     hotline |    .062783   .0642277     0.98   0.330    -.0640603    .1896263
     verdade |   .0286366   .0633606     0.45   0.652    -.0964942    .1537675
       _cons |   .6989619   .0472254    14.80   0.000     .6056965    .7922274
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.32
            Prob > F =    0.8088
.80883453


note: results saved to balance.xml

Linear regression                                      Number of obs =     864
                                                       F(  1,    81) =    0.05
                                                       Prob > F      =  0.8290
                                                       R-squared     =  0.0001
                                                       Root MSE      =   181.4

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 expenditure |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   3.525833   16.26987     0.22   0.829     -28.8461    35.89776
       _cons |   127.2035   12.26294    10.37   0.000     102.8041    151.6029
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     844
                                                       F(  1,    80) =    0.00
                                                       Prob > F      =  0.9571
                                                       R-squared     =  0.0000
                                                       Root MSE      =  153.11

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 expenditure |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.8599857   15.92624    -0.05   0.957    -32.55421    30.83424
       _cons |   127.2035   12.26404    10.37   0.000     102.7973    151.6097
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     840
                                                       F(  1,    79) =    0.05
                                                       Prob > F      =  0.8308
                                                       R-squared     =  0.0001
                                                       Root MSE      =  150.65

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 expenditure |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   3.431133   16.00815     0.21   0.831     -28.4323    35.29457
       _cons |   127.2035   12.26503    10.37   0.000     102.7905    151.6164
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1676
                                                       F(  3,   160) =    0.05
                                                       Prob > F      =  0.9865
                                                       R-squared     =  0.0001
                                                       Root MSE      =  164.09

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 expenditure |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   3.525833   16.22595     0.22   0.828    -28.51881    35.57048
     hotline |  -.8599857   15.88182    -0.05   0.957    -32.22501    30.50504
     verdade |   3.431133   15.96221     0.21   0.830    -28.09266    34.95493
       _cons |   127.2035   12.22983    10.40   0.000     103.0508    151.3562
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.05
            Prob > F =    0.9865
.98651114

Linear regression                                      Number of obs =     563
                                                       F(  1,    81) =    0.02
                                                       Prob > F      =  0.8980
                                                       R-squared     =  0.0000
                                                       Root MSE      =  158.89

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 expenditure |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   2.117224   16.46462     0.13   0.898     -30.6422    34.87664
       _cons |   121.4974   11.25835    10.79   0.000     99.09687     143.898
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     560
                                                       F(  1,    80) =    0.35
                                                       Prob > F      =  0.5560
                                                       R-squared     =  0.0011
                                                       Root MSE      =  151.49

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 expenditure |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   9.897279   16.73868     0.59   0.556    -23.41375    43.20831
       _cons |   121.4974   11.25926    10.79   0.000     99.09081    143.9041
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     545
                                                       F(  1,    79) =    0.11
                                                       Prob > F      =  0.7467
                                                       R-squared     =  0.0003
                                                       Root MSE      =  142.83

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 expenditure |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   5.146576   15.88153     0.32   0.747    -26.46482    36.75798
       _cons |   121.4974   11.26042    10.79   0.000     99.08415    143.9108
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1108
                                                       F(  3,   160) =    0.13
                                                       Prob > F      =  0.9422
                                                       R-squared     =  0.0006
                                                       Root MSE      =  154.59

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 expenditure |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   2.117224   16.42263     0.13   0.898    -30.31585     34.5503
     hotline |   9.897279   16.69464     0.59   0.554    -23.07299    42.86755
     verdade |   5.146576   15.83812     0.32   0.746    -26.13215    36.42531
       _cons |   121.4974   11.22964    10.82   0.000     99.32002    143.6749
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.13
            Prob > F =    0.9422
.94221242


note: results saved to balance.xml

. 
. foreach i in $demo6 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0, cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & drops2==0, cluster(ea)
 14.         estimates store `i'_10
 15.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & drops2==0, cluster(ea)
 16.         estimates store `i'_11
 17.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & drops2==0, cluster(ea)
 18.         estimates store `i'_12
 19.         regress `i' civiceduc hotline verdade if time==0 & drops2==0, cluster(ea)
 20.         test civiceduc hotline verdade
 21.         scalar define f`i'_4=r(p)
 22.         display f`i'_4
 23. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3" + " `i'_10" + " `i'_11" + " `i'
> _12"
 24.         xml_tab $list1, below save(balance.xml) append sheet("bal `i'")
 25.         estimates clear
 26. 
. }

Linear regression                                      Number of obs =     763
                                                       F(  1,    72) =    1.46
                                                       Prob > F      =  0.2314
                                                       R-squared     =  0.0126
                                                       Root MSE      =  .82258

                                    (Std. Err. adjusted for 73 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
netmean_dist |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1856747   .1538308     1.21   0.231    -.1209814    .4923308
       _cons |   1.155752   .0912268    12.67   0.000     .9738949    1.337609
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     730
                                                       F(  1,    69) =    0.03
                                                       Prob > F      =  0.8548
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .68541

                                    (Std. Err. adjusted for 70 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
netmean_dist |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0244897   .1333263     0.18   0.855     -.241489    .2904685
       _cons |   1.155752   .0912567    12.66   0.000     .9737001    1.337804
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     737
                                                       F(  1,    70) =    0.15
                                                       Prob > F      =  0.7026
                                                       R-squared     =  0.0013
                                                       Root MSE      =  .71445

                                    (Std. Err. adjusted for 71 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
netmean_dist |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0518958   .1353846     0.38   0.703    -.2181204    .3219119
       _cons |   1.155752   .0912468    12.67   0.000     .9737662    1.337738
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1506
                                                       F(  3,   143) =    0.53
                                                       Prob > F      =  0.6649
                                                       R-squared     =  0.0088
                                                       Root MSE      =   .7772

                                   (Std. Err. adjusted for 144 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
netmean_dist |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1856747   .1533591     1.21   0.228    -.1174689    .4888183
     hotline |   .0244897    .132874     0.18   0.854    -.2381612    .2871407
     verdade |   .0518958   .1349399     0.38   0.701     -.214839    .3186305
       _cons |   1.155752    .090947    12.71   0.000     .9759779    1.335526
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   143) =    0.53
            Prob > F =    0.6649
.66490093

Linear regression                                      Number of obs =     503
                                                       F(  1,    72) =    0.40
                                                       Prob > F      =  0.5277
                                                       R-squared     =  0.0039
                                                       Root MSE      =  .81602

                                    (Std. Err. adjusted for 73 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
netmean_dist |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1019624   .1606731     0.63   0.528    -.2183335    .4222583
       _cons |   1.212484   .1009644    12.01   0.000     1.011215    1.413753
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     491
                                                       F(  1,    69) =    0.09
                                                       Prob > F      =  0.7610
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .71749

                                    (Std. Err. adjusted for 70 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
netmean_dist |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   -.044841   .1468609    -0.31   0.761    -.3378205    .2481386
       _cons |   1.212484   .1009969    12.01   0.000     1.011001    1.413967
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     481
                                                       F(  1,    70) =    0.19
                                                       Prob > F      =  0.6642
                                                       R-squared     =  0.0019
                                                       Root MSE      =  .71969

                                    (Std. Err. adjusted for 71 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
netmean_dist |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   -.062177   .1426357    -0.44   0.664     -.346655    .2223009
       _cons |   1.212484   .1009887    12.01   0.000     1.011068      1.4139
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     997
                                                       F(  3,   143) =    0.40
                                                       Prob > F      =  0.7545
                                                       R-squared     =  0.0070
                                                       Root MSE      =  .77324

                                   (Std. Err. adjusted for 144 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
netmean_dist |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1019624   .1602076     0.64   0.526    -.2147187    .4186436
     hotline |   -.044841   .1463883    -0.31   0.760    -.3342057    .2445237
     verdade |   -.062177   .1421882    -0.44   0.663    -.3432393    .2188852
       _cons |   1.212484   .1006719    12.04   0.000     1.013487    1.411481
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   143) =    0.40
            Prob > F =    0.7545
.75453381


note: results saved to balance.xml

. 
. global list2=""

. matrix define fpvalue_1=(fsex_1 \ fage_1 \ fhead_1 \ fhousen_1 \ fsingle_1 \ fmarriedunion_1 \
>  fnoschl_1 \ finformalschl_1 \ flit_1 \ fprim5y_1 \ fsec10y_1 \ fchang_1 \ fmacua_1 \ flomue_1
>  \ fchuabo_1 \ fchironga_1 \ fmaconde_1 \ fcathol_1 \ fprotest_1 \ fmuslim_1 \ fjob_1 \ fagric
> _1 \ fcom_1 \ fart_1 \ fman_1 \ fassal_1 \ ftea_1 \ fpuboff_1 \ fstud_1 \ fdom_1 \ fhouse_1 \ 
> fland_1 \ fcattle_1 \ fcel_1 \ fexpenditure_1 \ fnetmean_dist_1)

. matrix rownames fpvalue_1 = "sex" "age" "head" "housen" "single" "marriedunion" "noschl" "info
> rmalschl" "lit" "prim5y" "sec10y" "chang" "macua" "lomue" "chuabo" "chironga" "maconde" "catho
> l" "protest" "muslim" "job" "agric" "com" "art" "man" "assal" "tea" "puboff" "stud" "dom" "hou
> se" "land" "cattle" "cel" "expenditure" "netmean_dist"

. matrix define fpvalue_4=(fsex_4 \ fage_4 \ fhead_4 \ fhousen_4 \ fsingle_4 \ fmarriedunion_4 \
>  fnoschl_4 \ finformalschl_4 \ flit_4 \ fprim5y_4 \ fsec10y_4 \ fchang_4 \ fmacua_4 \ flomue_4
>  \ fchuabo_4 \ fchironga_4 \ fmaconde_4 \ fcathol_4 \ fprotest_4 \ fmuslim_4 \ fjob_4 \ fagric
> _4 \ fcom_4 \ fart_4 \ fman_4 \ fassal_4 \ ftea_4 \ fpuboff_4 \ fstud_4 \ fdom_4 \ fhouse_4 \ 
> fland_4 \ fcattle_4 \ fcel_4 \ fexpenditure_4 \ fnetmean_dist_4)

. matrix fpvalue= (fpvalue_1, fpvalue_4)

. global list2="$list2" + " fpvalue"

. xml_tab $list2, save(balance.xml) append sheet("fpvalue demo") 


note: results saved to balance.xml

. estimates clear

. 
. **********************************************
. *****  BALANCE OF OFFICIAL RESULTS 2004  *****
. **********************************************
. 
. set more off

. 
. global ballot1="voterspres04 bsturnoutpres04 bsguebas04 bsdhlakama04 bsnullpres04 bsblankpres0
> 4"

. global ballot2="bsturnoutparl04 bsfrelimo04 bsrenamo04 bsnullparl04 bsblankparl04"

. global ballot3="voterspres09"

. 
. foreach i in $ballot1 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & v==1
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & v==1
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & v==1
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0 & v==1
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'=r(p)
 12.         display f`i'
 13.         
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 14.         
.         xml_tab $list1, below save(balance.xml) append sheet("bal `i'")
 15.         estimates clear
 16. 
. }

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    0.11
       Model |  835472.305     1  835472.305           Prob > F      =  0.7430
    Residual |   617171280    80     7714641           R-squared     =  0.0014
-------------+------------------------------           Adj R-squared = -0.0111
       Total |   618006752    81  7629712.99           Root MSE      =  2777.5

------------------------------------------------------------------------------
voterspres04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -201.878   613.4525    -0.33   0.743    -1422.687    1018.931
       _cons |   4768.415   433.7764    10.99   0.000     3905.172    5631.657
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.01
       Model |  96914.1772     1  96914.1772           Prob > F      =  0.9237
    Residual |   829728418    79  10502891.4           R-squared     =  0.0001
-------------+------------------------------           Adj R-squared = -0.0125
       Total |   829825332    80  10372816.6           Root MSE      =  3240.8

------------------------------------------------------------------------------
voterspres04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   69.18537   720.2363     0.10   0.924    -1364.409     1502.78
       _cons |   4768.415   506.1305     9.42   0.000     3760.987    5775.842
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.10
       Model |  1060837.64     1  1060837.64           Prob > F      =  0.7506
    Residual |   812974398    78  10422748.7           R-squared     =  0.0013
-------------+------------------------------           Adj R-squared = -0.0115
       Total |   814035236    79  10304243.5           Root MSE      =  3228.4

------------------------------------------------------------------------------
voterspres04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   230.3802   722.1242     0.32   0.751    -1207.259    1668.019
       _cons |   4768.415   504.1958     9.46   0.000     3764.638    5772.191
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.12
       Model |  3857321.89     3  1285773.96           Prob > F      =  0.9472
    Residual |  1.6583e+09   157  10562508.4           R-squared     =  0.0023
-------------+------------------------------           Adj R-squared = -0.0167
       Total |  1.6622e+09   160  10388569.6           Root MSE      =    3250

------------------------------------------------------------------------------
voterspres04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -201.878   717.8052    -0.28   0.779    -1619.679    1215.923
     hotline |   69.18537   722.2776     0.10   0.924    -1357.449     1495.82
     verdade |   230.3802   726.9496     0.32   0.752    -1205.483    1666.243
       _cons |   4768.415   507.5649     9.39   0.000     3765.878    5770.951
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.12
            Prob > F =    0.9472
.9471882


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    0.51
       Model |  .007611825     1  .007611825           Prob > F      =  0.4792
    Residual |  1.20508425    80  .015063553           R-squared     =  0.0063
-------------+------------------------------           Adj R-squared = -0.0061
       Total |  1.21269607    81  .014971556           Root MSE      =  .12273

------------------------------------------------------------------------------
bsturnou~s04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0192694   .0271073    -0.71   0.479    -.0732147    .0346759
       _cons |   .4065071   .0191678    21.21   0.000      .368362    .4446522
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.03
       Model |  .000818505     1  .000818505           Prob > F      =  0.8541
    Residual |  1.90008612    79  .024051723           R-squared     =  0.0004
-------------+------------------------------           Adj R-squared = -0.0122
       Total |  1.90090462    80  .023761308           Root MSE      =  .15509

------------------------------------------------------------------------------
bsturnou~s04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0063582   .0344662    -0.18   0.854    -.0749615    .0622451
       _cons |   .4065071   .0242204    16.78   0.000     .3582976    .4547166
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.88
       Model |  .015543708     1  .015543708           Prob > F      =  0.3507
    Residual |  1.37525825    78  .017631516           R-squared     =  0.0112
-------------+------------------------------           Adj R-squared = -0.0015
       Total |  1.39080195    79  .017605088           Root MSE      =  .13278

------------------------------------------------------------------------------
bsturnou~s04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0278868   .0297006    -0.94   0.351    -.0870162    .0312426
       _cons |   .4065071   .0207373    19.60   0.000     .3652223     .447792
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.31
       Model |  .018938653     3  .006312884           Prob > F      =  0.8190
    Residual |  3.20990467   157  .020445253           R-squared     =  0.0059
-------------+------------------------------           Adj R-squared = -0.0131
       Total |  3.22884332   160  .020180271           Root MSE      =  .14299

------------------------------------------------------------------------------
bsturnou~s04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0192694   .0315805    -0.61   0.543    -.0816469    .0431081
     hotline |  -.0063582   .0317773    -0.20   0.842    -.0691243     .056408
     verdade |  -.0278868   .0319828    -0.87   0.385    -.0910589    .0352854
       _cons |   .4065071   .0223308    18.20   0.000     .3623996    .4506147
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.31
            Prob > F =    0.8190
.81902348


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    0.69
       Model |  .030087248     1  .030087248           Prob > F      =  0.4102
    Residual |   3.5119963    80  .043899954           R-squared     =  0.0085
-------------+------------------------------           Adj R-squared = -0.0039
       Total |  3.54208355    81  .043729427           Root MSE      =  .20952

------------------------------------------------------------------------------
  bsguebas04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0383102   .0462759     0.83   0.410    -.0537818    .1304022
       _cons |   .7139474    .032722    21.82   0.000     .6488285    .7790662
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.02
       Model |  .001085041     1  .001085041           Prob > F      =  0.8771
    Residual |  3.56061697    79  .045071101           R-squared     =  0.0003
-------------+------------------------------           Adj R-squared = -0.0123
       Total |  3.56170201    80  .044521275           Root MSE      =   .2123

------------------------------------------------------------------------------
  bsguebas04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0073205   .0471813     0.16   0.877    -.0865914    .1012325
       _cons |   .7139474   .0331556    21.53   0.000     .6479527     .779942
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.06
       Model |  .002651739     1  .002651739           Prob > F      =  0.8111
    Residual |  3.59672672    78  .046111881           R-squared     =  0.0007
-------------+------------------------------           Adj R-squared = -0.0121
       Total |  3.59937846    79  .045561753           Root MSE      =  .21474

------------------------------------------------------------------------------
  bsguebas04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0115182   .0480316    -0.24   0.811    -.1071418    .0841054
       _cons |   .7139474   .0335362    21.29   0.000     .6471818    .7807129
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.42
       Model |  .055067863     3  .018355954           Prob > F      =  0.7358
    Residual |  6.79137058   157  .043257137           R-squared     =  0.0080
-------------+------------------------------           Adj R-squared = -0.0109
       Total |  6.84643844   160   .04279024           Root MSE      =  .20798

------------------------------------------------------------------------------
  bsguebas04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0383102   .0459359     0.83   0.406    -.0524218    .1290422
     hotline |   .0073205   .0462221     0.16   0.874    -.0839768    .0986179
     verdade |  -.0115182   .0465211    -0.25   0.805    -.1034061    .0803697
       _cons |   .7139474   .0324816    21.98   0.000     .6497901    .7781046
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.42
            Prob > F =    0.7358
.73580999


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    0.47
       Model |  .016024842     1  .016024842           Prob > F      =  0.4935
    Residual |   2.7088648    80   .03386081           R-squared     =  0.0059
-------------+------------------------------           Adj R-squared = -0.0065
       Total |  2.72488965    81  .033640613           Root MSE      =  .18401

------------------------------------------------------------------------------
bsdhlakama04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0279589   .0406417    -0.69   0.493    -.1088384    .0529206
       _cons |   .1879982    .028738     6.54   0.000     .1308078    .2451887
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.03
       Model |  .001047279     1  .001047279           Prob > F      =  0.8632
    Residual |  2.76865233    79  .035046232           R-squared     =  0.0004
-------------+------------------------------           Adj R-squared = -0.0123
       Total |   2.7696996    80  .034621245           Root MSE      =  .18721

------------------------------------------------------------------------------
bsdhlakama04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   -.007192   .0416046    -0.17   0.863    -.0900039    .0756198
       _cons |   .1879982   .0292367     6.43   0.000      .129804    .2461925
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.18
       Model |  .006974227     1  .006974227           Prob > F      =  0.6684
    Residual |  2.94287751    78  .037729199           R-squared     =  0.0024
-------------+------------------------------           Adj R-squared = -0.0104
       Total |  2.94985174    79  .037339895           Root MSE      =  .19424

------------------------------------------------------------------------------
bsdhlakama04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0186797    .043447     0.43   0.668    -.0678166    .1051759
       _cons |   .1879982   .0303352     6.20   0.000     .1276055     .248391
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.44
       Model |  .044635547     3  .014878516           Prob > F      =  0.7251
    Residual |  5.31645575   157  .033862775           R-squared     =  0.0083
-------------+------------------------------           Adj R-squared = -0.0106
       Total |   5.3610913   160  .033506821           Root MSE      =  .18402

------------------------------------------------------------------------------
bsdhlakama04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0279589   .0406429    -0.69   0.493    -.1082362    .0523185
     hotline |   -.007192   .0408961    -0.18   0.861    -.0879696    .0735855
     verdade |   .0186797   .0411606     0.45   0.651    -.0626204    .0999797
       _cons |   .1879982   .0287388     6.54   0.000     .1312336    .2447629
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.44
            Prob > F =    0.7251
.72514909


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    1.25
       Model |  .000260991     1  .000260991           Prob > F      =  0.2675
    Residual |  .016745515    80  .000209319           R-squared     =  0.0153
-------------+------------------------------           Adj R-squared =  0.0030
       Total |  .017006506    81  .000209957           Root MSE      =  .01447

------------------------------------------------------------------------------
bsnullpres04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0035681   .0031954    -1.12   0.267    -.0099272     .002791
       _cons |   .0345609   .0022595    15.30   0.000     .0300644    .0390575
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.09
       Model |  .000052897     1  .000052897           Prob > F      =  0.7594
    Residual |    .0442461    79  .000560077           R-squared     =  0.0012
-------------+------------------------------           Adj R-squared = -0.0114
       Total |  .044298996    80  .000553737           Root MSE      =  .02367

------------------------------------------------------------------------------
bsnullpres04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0016163   .0052595     0.31   0.759    -.0088524    .0120851
       _cons |   .0345609    .003696     9.35   0.000     .0272042    .0419176
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.25
       Model |  .000049337     1  .000049337           Prob > F      =  0.6193
    Residual |  .015469229    78  .000198323           R-squared     =  0.0032
-------------+------------------------------           Adj R-squared = -0.0096
       Total |  .015518566    79  .000196438           Root MSE      =  .01408

------------------------------------------------------------------------------
bsnullpres04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0015711     .00315    -0.50   0.619    -.0078422       .0047
       _cons |   .0345609   .0021994    15.71   0.000     .0301824    .0389395
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.54
       Model |  .000595898     3  .000198633           Prob > F      =  0.6542
    Residual |  .057533689   157  .000366457           R-squared     =  0.0103
-------------+------------------------------           Adj R-squared = -0.0087
       Total |  .058129586   160   .00036331           Root MSE      =  .01914

------------------------------------------------------------------------------
bsnullpres04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0035681    .004228    -0.84   0.400    -.0119192     .004783
     hotline |   .0016163   .0042543     0.38   0.705    -.0067868    .0100195
     verdade |  -.0015711   .0042819    -0.37   0.714    -.0100286    .0068864
       _cons |   .0345609   .0029896    11.56   0.000     .0286558     .040466
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.54
            Prob > F =    0.6542
.65421371


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    1.03
       Model |  .000498803     1  .000498803           Prob > F      =  0.3125
    Residual |  .038623385    80  .000482792           R-squared     =  0.0127
-------------+------------------------------           Adj R-squared =  0.0004
       Total |  .039122188    81   .00048299           Root MSE      =  .02197

------------------------------------------------------------------------------
bsblankpr~04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0049327   .0048529    -1.02   0.312    -.0145904    .0047249
       _cons |   .0323119   .0034315     9.42   0.000     .0254829    .0391409
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.05
       Model |  .000038416     1  .000038416           Prob > F      =  0.8193
    Residual |  .057788837    79  .000731504           R-squared     =  0.0007
-------------+------------------------------           Adj R-squared = -0.0120
       Total |  .057827252    80  .000722841           Root MSE      =  .02705

------------------------------------------------------------------------------
bsblankpr~04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0013774   .0060108    -0.23   0.819    -.0133416    .0105867
       _cons |   .0323119   .0042239     7.65   0.000     .0239044    .0407194
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.51
       Model |  .000272477     1  .000272477           Prob > F      =  0.4768
    Residual |   .04159058    78  .000533213           R-squared     =  0.0065
-------------+------------------------------           Adj R-squared = -0.0062
       Total |  .041863057    79  .000529912           Root MSE      =  .02309

------------------------------------------------------------------------------
bsblankpr~04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0036922    .005165    -0.71   0.477    -.0139749    .0065905
       _cons |   .0323119   .0036063     8.96   0.000     .0251324    .0394914
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.38
       Model |  .000604725     3  .000201575           Prob > F      =  0.7681
    Residual |  .083444472   157  .000531493           R-squared     =  0.0072
-------------+------------------------------           Adj R-squared = -0.0118
       Total |  .084049197   160  .000525307           Root MSE      =  .02305

------------------------------------------------------------------------------
bsblankpr~04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0049327   .0050918    -0.97   0.334      -.01499    .0051246
     hotline |  -.0013774   .0051235    -0.27   0.788    -.0114974    .0087425
     verdade |  -.0036922   .0051567    -0.72   0.475    -.0138776    .0064932
       _cons |   .0323119   .0036005     8.97   0.000     .0252003    .0394235
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.38
            Prob > F =    0.7681
.7680828


note: results saved to balance.xml

. 
. foreach i in $ballot2 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & v==1
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & v==1
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & v==1
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0 & v==1
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'=r(p)
 12.         display f`i'
 13.         
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 14.         
.         xml_tab $list1, below save(balance.xml) append sheet("bal `i'")
 15.         estimates clear
 16. 
. }

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    1.01
       Model |  .015254647     1  .015254647           Prob > F      =  0.3180
    Residual |  1.20874748    80  .015109344           R-squared     =  0.0125
-------------+------------------------------           Adj R-squared =  0.0001
       Total |  1.22400213    81  .015111137           Root MSE      =  .12292

------------------------------------------------------------------------------
bsturnou~l04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0272787   .0271485    -1.00   0.318     -.081306    .0267485
       _cons |   .4141045   .0191969    21.57   0.000     .3759015    .4523076
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.02
       Model |  .000430668     1  .000430668           Prob > F      =  0.8978
    Residual |  2.04892285    79  .025935732           R-squared     =  0.0002
-------------+------------------------------           Adj R-squared = -0.0124
       Total |  2.04935351    80  .025616919           Root MSE      =  .16105

------------------------------------------------------------------------------
bsturnou~l04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   -.004612   .0357907    -0.13   0.898    -.0758516    .0666275
       _cons |   .4141045   .0251511    16.46   0.000     .3640425    .4641666
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    1.18
       Model |  .020194317     1  .020194317           Prob > F      =  0.2801
    Residual |  1.33128206    78  .017067719           R-squared     =  0.0149
-------------+------------------------------           Adj R-squared =  0.0023
       Total |  1.35147638    79  .017107296           Root MSE      =  .13064

------------------------------------------------------------------------------
bsturnou~l04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   -.031786   .0292219    -1.09   0.280    -.0899623    .0263904
       _cons |   .4141045   .0204031    20.30   0.000     .3734851     .454724
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.48
       Model |  .030610756     3  .010203585           Prob > F      =  0.6937
    Residual |  3.30784033   157  .021069047           R-squared     =  0.0092
-------------+------------------------------           Adj R-squared = -0.0098
       Total |  3.33845109   160  .020865319           Root MSE      =  .14515

------------------------------------------------------------------------------
bsturnou~l04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0272787   .0320587    -0.85   0.396    -.0906007    .0360432
     hotline |   -.004612   .0322584    -0.14   0.886    -.0683285    .0591044
     verdade |   -.031786   .0324671    -0.98   0.329    -.0959146    .0323427
       _cons |   .4141045   .0226689    18.27   0.000     .3693292    .4588799
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.48
            Prob > F =    0.6937
.69367437


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    0.75
       Model |  .032196194     1  .032196194           Prob > F      =  0.3881
    Residual |  3.42003561    80  .042750445           R-squared     =  0.0093
-------------+------------------------------           Adj R-squared = -0.0031
       Total |  3.45223181    81  .042620146           Root MSE      =  .20676

------------------------------------------------------------------------------
 bsfrelimo04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0396301    .045666     0.87   0.388    -.0512482    .1305084
       _cons |   .6727377   .0322908    20.83   0.000      .608477    .7369983
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.12
       Model |  .004989671     1  .004989671           Prob > F      =  0.7348
    Residual |  3.41050218    79  .043170914           R-squared     =  0.0015
-------------+------------------------------           Adj R-squared = -0.0112
       Total |  3.41549185    80  .042693648           Root MSE      =  .20778

------------------------------------------------------------------------------
 bsfrelimo04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0156984    .046176     0.34   0.735    -.0762126    .1076094
       _cons |   .6727377   .0324492    20.73   0.000     .6081492    .7373261
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.04
       Model |  .001888106     1  .001888106           Prob > F      =  0.8363
    Residual |  3.42438936    78  .043902428           R-squared     =  0.0006
-------------+------------------------------           Adj R-squared = -0.0123
       Total |  3.42627747    79  .043370601           Root MSE      =  .20953

------------------------------------------------------------------------------
 bsfrelimo04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0097193   .0468668    -0.21   0.836    -.1030238    .0835853
       _cons |   .6727377   .0327229    20.56   0.000     .6075913     .737884
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.46
       Model |  .056127748     3  .018709249           Prob > F      =  0.7140
    Residual |  6.45309232   157  .041102499           R-squared     =  0.0086
-------------+------------------------------           Adj R-squared = -0.0103
       Total |  6.50922006   160  .040682625           Root MSE      =  .20274

------------------------------------------------------------------------------
 bsfrelimo04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0396301   .0447772     0.89   0.377    -.0488134    .1280736
     hotline |   .0156984   .0450562     0.35   0.728    -.0732961     .104693
     verdade |  -.0097193   .0453477    -0.21   0.831    -.0992895    .0798509
       _cons |   .6727377   .0316623    21.25   0.000     .6101987    .7352767
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.46
            Prob > F =    0.7140
.71400245


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    0.60
       Model |  .017119518     1  .017119518           Prob > F      =  0.4391
    Residual |  2.26482495    80  .028310312           R-squared     =  0.0075
-------------+------------------------------           Adj R-squared = -0.0049
       Total |  2.28194447    81  .028172154           Root MSE      =  .16826

------------------------------------------------------------------------------
  bsrenamo04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0288981   .0371617    -0.78   0.439    -.1028522     .045056
       _cons |   .1790003   .0262773     6.81   0.000     .1267068    .2312937
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.13
       Model |  .003545573     1  .003545573           Prob > F      =  0.7227
    Residual |  2.20931984    79  .027966074           R-squared     =  0.0016
-------------+------------------------------           Adj R-squared = -0.0110
       Total |  2.21286541    80  .027660818           Root MSE      =  .16723

------------------------------------------------------------------------------
  bsrenamo04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0132332   .0371652    -0.36   0.723    -.0872086    .0607423
       _cons |   .1790003    .026117     6.85   0.000     .1270156    .2309849
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.12
       Model |   .00382428     1   .00382428           Prob > F      =  0.7273
    Residual |  2.43572741    78  .031227274           R-squared     =  0.0016
-------------+------------------------------           Adj R-squared = -0.0112
       Total |  2.43955169    79  .030880401           Root MSE      =  .17671

------------------------------------------------------------------------------
  bsrenamo04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0138323   .0395264     0.35   0.727    -.0648588    .0925235
       _cons |   .1790003   .0275978     6.49   0.000     .1240572    .2339433
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.48
       Model |  .040134981     3  .013378327           Prob > F      =  0.6942
    Residual |  4.34442028   157  .027671467           R-squared     =  0.0092
-------------+------------------------------           Adj R-squared = -0.0098
       Total |  4.38455526   160   .02740347           Root MSE      =  .16635

------------------------------------------------------------------------------
  bsrenamo04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0288981     .03674    -0.79   0.433    -.1014665    .0436704
     hotline |  -.0132332   .0369689    -0.36   0.721    -.0862538    .0597874
     verdade |   .0138323    .037208     0.37   0.711    -.0596606    .0873253
       _cons |   .1790003   .0259791     6.89   0.000     .1276866    .2303139
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.48
            Prob > F =    0.6942
.69424512


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    2.52
       Model |  .000885666     1  .000885666           Prob > F      =  0.1165
    Residual |  .028134709    80  .000351684           R-squared     =  0.0305
-------------+------------------------------           Adj R-squared =  0.0184
       Total |  .029020375    81  .000358276           Root MSE      =  .01875

------------------------------------------------------------------------------
bsnullparl04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0065729   .0041419    -1.59   0.116    -.0148156    .0016697
       _cons |   .0385365   .0029288    13.16   0.000     .0327081    .0443649
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.07
       Model |  .000073617     1  .000073617           Prob > F      =  0.7940
    Residual |  .084741623    79  .001072679           R-squared     =  0.0009
-------------+------------------------------           Adj R-squared = -0.0118
       Total |  .084815239    80   .00106019           Root MSE      =  .03275

------------------------------------------------------------------------------
bsnullparl04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0019068   .0072787     0.26   0.794    -.0125811    .0163948
       _cons |   .0385365    .005115     7.53   0.000     .0283554    .0487176
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.50
       Model |  .000192065     1  .000192065           Prob > F      =  0.4808
    Residual |   .02984911    78  .000382681           R-squared     =  0.0064
-------------+------------------------------           Adj R-squared = -0.0063
       Total |  .030041175    79  .000380268           Root MSE      =  .01956

------------------------------------------------------------------------------
bsnullparl04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0030999   .0043756    -0.71   0.481    -.0118111    .0056113
       _cons |   .0385365   .0030551    12.61   0.000     .0324542    .0446187
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.82
       Model |  .001678696     3  .000559565           Prob > F      =  0.4857
    Residual |  .107394036   157  .000684038           R-squared     =  0.0154
-------------+------------------------------           Adj R-squared = -0.0034
       Total |  .109072731   160  .000681705           Root MSE      =  .02615

------------------------------------------------------------------------------
bsnullparl04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0065729   .0057765    -1.14   0.257    -.0179826    .0048367
     hotline |   .0019068   .0058125     0.33   0.743    -.0095739    .0133875
     verdade |  -.0030999   .0058501    -0.53   0.597    -.0146549    .0084551
       _cons |   .0385365   .0040846     9.43   0.000     .0304686    .0466043
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.82
            Prob > F =    0.4857
.48571023


note: results saved to balance.xml

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    0.42
       Model |  .000517829     1  .000517829           Prob > F      =  0.5203
    Residual |  .099336039    80    .0012417           R-squared     =  0.0052
-------------+------------------------------           Adj R-squared = -0.0072
       Total |  .099853868    81  .001232764           Root MSE      =  .03524

------------------------------------------------------------------------------
bsblankpa~04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0050259   .0077827    -0.65   0.520     -.020514    .0104622
       _cons |    .054682   .0055032     9.94   0.000     .0437302    .0656337
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.38
       Model |  .000593539     1  .000593539           Prob > F      =  0.5384
    Residual |  .122796402    79  .001554385           R-squared     =  0.0048
-------------+------------------------------           Adj R-squared = -0.0078
       Total |  .123389941    80  .001542374           Root MSE      =  .03943

------------------------------------------------------------------------------
bsblankpa~04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0054143   .0087619    -0.62   0.538    -.0228545    .0120259
       _cons |    .054682   .0061573     8.88   0.000     .0424263    .0669377
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.27
       Model |  .000331615     1  .000331615           Prob > F      =  0.6051
    Residual |  .095931625    78  .001229893           R-squared     =  0.0034
-------------+------------------------------           Adj R-squared = -0.0093
       Total |  .096263241    79  .001218522           Root MSE      =  .03507

------------------------------------------------------------------------------
bsblankpa~04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0040732   .0078443    -0.52   0.605      -.01969    .0115436
       _cons |    .054682    .005477     9.98   0.000     .0437781    .0655858
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.20
       Model |  .000755108     3  .000251703           Prob > F      =  0.8945
    Residual |  .195130787   157  .001242871           R-squared     =  0.0039
-------------+------------------------------           Adj R-squared = -0.0152
       Total |  .195885895   160  .001224287           Root MSE      =  .03525

------------------------------------------------------------------------------
bsblankpa~04 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0050259   .0077864    -0.65   0.520    -.0204055    .0103537
     hotline |  -.0054143   .0078349    -0.69   0.491    -.0208898    .0100611
     verdade |  -.0040732   .0078856    -0.52   0.606    -.0196487    .0115023
       _cons |    .054682   .0055058     9.93   0.000      .043807     .065557
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.20
            Prob > F =    0.8945
.89452875


note: results saved to balance.xml

. 
. foreach i in $ballot3 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & v==1
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & v==1
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & v==1
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0 & v==1
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'=r(p)
 12.         display f`i'
 13.         
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 14.         
.         xml_tab $list1, below save(balance.xml) append sheet("bal `i'")
 15.         estimates clear
 16. 
. }

      Source |       SS       df       MS              Number of obs =      82
-------------+------------------------------           F(  1,    80) =    0.01
       Model |  36667.7561     1  36667.7561           Prob > F      =  0.9394
    Residual |   503819359    80  6297741.99           R-squared     =  0.0001
-------------+------------------------------           Adj R-squared = -0.0124
       Total |   503856027    81  6220444.78           Root MSE      =  2509.5

------------------------------------------------------------------------------
voterspres09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   42.29268   554.2625     0.08   0.939    -1060.725     1145.31
       _cons |   4013.146   391.9228    10.24   0.000     3233.195    4793.098
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      81
-------------+------------------------------           F(  1,    79) =    0.01
       Model |  49954.0389     1  49954.0389           Prob > F      =  0.9271
    Residual |   468977261    79  5936421.03           R-squared     =  0.0001
-------------+------------------------------           Adj R-squared = -0.0126
       Total |   469027215    80  5862840.19           Root MSE      =  2436.5

------------------------------------------------------------------------------
voterspres09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -49.67134   541.4806    -0.09   0.927    -1127.462    1028.119
       _cons |   4013.146   380.5138    10.55   0.000     3255.753     4770.54
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =      80
-------------+------------------------------           F(  1,    78) =    0.36
       Model |  2857557.39     1  2857557.39           Prob > F      =  0.5512
    Residual |   622214607    78  7977110.34           R-squared     =  0.0046
-------------+------------------------------           Adj R-squared = -0.0082
       Total |   625072164    79  7912305.87           Root MSE      =  2824.4

------------------------------------------------------------------------------
voterspres09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   378.1101   631.7475     0.60   0.551    -879.6027    1635.823
       _cons |   4013.146   441.0937     9.10   0.000     3134.996    4891.296
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  3,   157) =    0.20
       Model |  4443027.84     3  1481009.28           Prob > F      =  0.8961
    Residual |  1.1611e+09   157  7395544.11           R-squared     =  0.0038
-------------+------------------------------           Adj R-squared = -0.0152
       Total |  1.1655e+09   160  7284646.58           Root MSE      =  2719.5

------------------------------------------------------------------------------
voterspres09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   42.29268   600.6315     0.07   0.944    -1144.068    1228.654
     hotline |  -49.67134   604.3738    -0.08   0.935    -1243.424    1144.081
     verdade |   378.1101   608.2832     0.62   0.535    -823.3643    1579.584
       _cons |   4013.146   424.7106     9.45   0.000     3174.262     4852.03
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   157) =    0.20
            Prob > F =    0.8961
.8960824


note: results saved to balance.xml

. 
. global list=""

. matrix define fpvalue=(fvoterspres04 \ fbsturnoutpres04 \ fbsguebas04 \ fbsdhlakama04 \ fbsnul
> lpres04 \ fbsblankpres04 \ fbsturnoutparl04 \ fbsfrelimo04 \ fbsrenamo04 \ fbsnullparl04 \ fbs
> blankparl04 \ fvoterspres09)

. matrix rownames fpvalue = "voterspres04" "bsturnoutpres04" "bsguebas04" "bsdhlakama04" "bsnull
> pres04" "bsblankpres04" "bsturnoutparl04" "bsfrelimo04" "bsrenamo04" "bsnullparl04" "bsblankpa
> rl04" "voterspres09"

. global list="$list" + " fpvalue"

. xml_tab $list, save(balance.xml) append sheet("fpvalue ballots") 


note: results saved to balance.xml

. estimates clear

. 
. *************************************************
. *****  BALANCE OF BASELINE SURVEY OUTCOMES  *****
. *************************************************
. 
. capture gen zsctcne=zsctrustcne

. capture gen zsccne=zscindepcne

. capture gen zscff4=zscfreefair2004

. capture gen zsccount=zscvcount2009

. capture gen zscff9_3=zscfreefair2009_3

. capture gen zscff9_4=zscfreefair2009_4

. 
. set more off

. 
. global votint="turnoutresp guebas2 dlakhama2 simango2 frelimo2 renamo2"

. global votpast="turnout2004 guebas20042 dlakhama20042 frelimo20042 renamo20042"

. global survey="zsctcne zsccne zscff4 zsccount zscff9_3 zscff9_4"

. 
. foreach i in $votint {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0, cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & drops2==0, cluster(ea)
 14.         estimates store `i'_10
 15.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & drops2==0, cluster(ea)
 16.         estimates store `i'_11
 17.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & drops2==0, cluster(ea)
 18.         estimates store `i'_12
 19.         regress `i' civiceduc hotline verdade if time==0 & drops2==0, cluster(ea)
 20.         test civiceduc hotline verdade
 21.         scalar define f`i'_4=r(p)
 22.         display f`i'_4
 23. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3" + " `i'_10" + " `i'_11" + " `i'
> _12"
 24.         xml_tab $list1, below save(balance.xml) append sheet("bal `i'")
 25.         estimates clear
 26. 
. }

Linear regression                                      Number of obs =     874
                                                       F(  1,    81) =    2.08
                                                       Prob > F      =  0.1533
                                                       R-squared     =  0.0025
                                                       Root MSE      =  .13404

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 turnoutresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0134793   .0093514     1.44   0.153    -.0051271    .0320856
       _cons |       .975   .0072588   134.32   0.000     .9605572    .9894428
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     863
                                                       F(  1,    80) =    0.84
                                                       Prob > F      =  0.3623
                                                       R-squared     =  0.0009
                                                       Root MSE      =  .14301

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 turnoutresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0084515   .0092237     0.92   0.362    -.0099042    .0268073
       _cons |       .975   .0072595   134.31   0.000     .9605532    .9894468
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     857
                                                       F(  1,    79) =    0.12
                                                       Prob > F      =  0.7350
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .16179

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 turnoutresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   -.003777   .0111199    -0.34   0.735    -.0259106    .0183566
       _cons |       .975     .00726   134.30   0.000     .9605492    .9894508
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1714
                                                       F(  3,   160) =    1.28
                                                       Prob > F      =  0.2842
                                                       R-squared     =  0.0023
                                                       Root MSE      =  .14143

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 turnoutresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0134793    .009326     1.45   0.150    -.0049387    .0318972
     hotline |   .0084515   .0091979     0.92   0.360    -.0097134    .0266165
     verdade |   -.003777   .0110879    -0.34   0.734    -.0256745    .0181205
       _cons |       .975   .0072392   134.68   0.000     .9607034    .9892966
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.28
            Prob > F =    0.2842
.28423621

Linear regression                                      Number of obs =     571
                                                       F(  1,    81) =    0.76
                                                       Prob > F      =  0.3873
                                                       R-squared     =  0.0014
                                                       Root MSE      =  .14932

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 turnoutresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0110678   .0127341     0.87   0.387     -.014269    .0364045
       _cons |   .9716312   .0090645   107.19   0.000     .9535956    .9896668
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     575
                                                       F(  1,    80) =    1.76
                                                       Prob > F      =  0.1884
                                                       R-squared     =  0.0026
                                                       Root MSE      =  .14301

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 turnoutresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0147169   .0110945     1.33   0.188    -.0073619    .0367957
       _cons |   .9716312   .0090652   107.18   0.000      .953591    .9896715
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     556
                                                       F(  1,    79) =    0.73
                                                       Prob > F      =  0.3968
                                                       R-squared     =  0.0011
                                                       Root MSE      =   .1513

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 turnoutresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0101206   .0118783     0.85   0.397    -.0135226    .0337638
       _cons |   .9716312   .0090661   107.17   0.000     .9535855    .9896769
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1138
                                                       F(  3,   160) =    0.59
                                                       Prob > F      =  0.6195
                                                       R-squared     =  0.0016
                                                       Root MSE      =  .13782

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 turnoutresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0110678   .0127013     0.87   0.385     -.014016    .0361515
     hotline |   .0147169   .0110652     1.33   0.185    -.0071358    .0365696
     verdade |   .0101206   .0118456     0.85   0.394    -.0132733    .0335146
       _cons |   .9716312   .0090412   107.47   0.000     .9537757    .9894867
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.59
            Prob > F =    0.6195
.61949985


note: results saved to balance.xml

Linear regression                                      Number of obs =     780
                                                       F(  1,    81) =    0.45
                                                       Prob > F      =  0.5052
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .32879

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0204425   .0305414     0.67   0.505    -.0403254    .0812104
       _cons |   .8664921   .0220217    39.35   0.000      .822676    .9103083
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     742
                                                       F(  1,    80) =    0.48
                                                       Prob > F      =  0.4902
                                                       R-squared     =  0.0012
                                                       Root MSE      =  .32828

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0223967   .0323121     0.69   0.490    -.0419063    .0866998
       _cons |   .8664921   .0220241    39.34   0.000     .8226629    .9103214
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     746
                                                       F(  1,    79) =    0.02
                                                       Prob > F      =  0.8921
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .33825

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |    .004387   .0322411     0.14   0.892    -.0597873    .0685612
       _cons |   .8664921   .0220257    39.34   0.000     .8226511    .9103332
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1504
                                                       F(  3,   160) =    0.25
                                                       Prob > F      =  0.8594
                                                       R-squared     =  0.0009
                                                       Root MSE      =   .3272

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0204425   .0304602     0.67   0.503    -.0397134    .0805984
     hotline |   .0223967   .0322226     0.70   0.488    -.0412397    .0860332
     verdade |    .004387   .0321494     0.14   0.892     -.059105    .0678789
       _cons |   .8664921   .0219631    39.45   0.000     .8231172    .9098671
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.25
            Prob > F =    0.8594
.85935347

Linear regression                                      Number of obs =     505
                                                       F(  1,    81) =    0.20
                                                       Prob > F      =  0.6544
                                                       R-squared     =  0.0006
                                                       Root MSE      =  .34404

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0164403     .03659     0.45   0.654    -.0563622    .0892429
       _cons |   .8547718    .026449    32.32   0.000     .8021465    .9073971
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     484
                                                       F(  1,    80) =    0.57
                                                       Prob > F      =  0.4530
                                                       R-squared     =  0.0020
                                                       Root MSE      =  .33685

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0300019   .0397889     0.75   0.453    -.0491805    .1091842
       _cons |   .8547718   .0264522    32.31   0.000     .8021302    .9074133
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     478
                                                       F(  1,    78) =    1.12
                                                       Prob > F      =  0.2924
                                                       R-squared     =  0.0028
                                                       Root MSE      =  .33389

                                    (Std. Err. adjusted for 79 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0355236   .0335113     1.06   0.292    -.0311923    .1022394
       _cons |   .8547718   .0264567    32.31   0.000     .8021005    .9074431
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     985
                                                       F(  3,   159) =    0.42
                                                       Prob > F      =  0.7410
                                                       R-squared     =  0.0017
                                                       Root MSE      =  .33097

                                   (Std. Err. adjusted for 160 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0164403   .0364998     0.45   0.653    -.0556467    .0885273
     hotline |   .0300019   .0396861     0.76   0.451     -.048378    .1083818
     verdade |   .0355236    .033419     1.06   0.289    -.0304789     .101526
       _cons |   .8547718   .0263839    32.40   0.000     .8026637    .9068799
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   159) =    0.42
            Prob > F =    0.7410
.74096261


note: results saved to balance.xml

Linear regression                                      Number of obs =     780
                                                       F(  1,    81) =    0.04
                                                       Prob > F      =  0.8387
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .12818

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0018811   .0092139     0.20   0.839    -.0164517    .0202139
       _cons |   .0157068   .0059561     2.64   0.010     .0038561    .0275575
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     742
                                                       F(  1,    80) =    0.32
                                                       Prob > F      =  0.5735
                                                       R-squared     =  0.0006
                                                       Root MSE      =   .1362

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0065154   .0115271     0.57   0.574    -.0164243    .0294551
       _cons |   .0157068   .0059567     2.64   0.010     .0038525    .0275611
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     746
                                                       F(  1,    79) =    0.40
                                                       Prob > F      =  0.5311
                                                       R-squared     =  0.0005
                                                       Root MSE      =  .13585

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0062712   .0099688     0.63   0.531    -.0135712    .0261136
       _cons |   .0157068   .0059572     2.64   0.010     .0038494    .0275643
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1504
                                                       F(  3,   160) =    0.19
                                                       Prob > F      =  0.9028
                                                       R-squared     =  0.0004
                                                       Root MSE      =  .13767

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0018811   .0091894     0.20   0.838     -.016267    .0200293
     hotline |   .0065154   .0114952     0.57   0.572    -.0161865    .0292173
     verdade |   .0062712   .0099405     0.63   0.529    -.0133602    .0259027
       _cons |   .0157068   .0059402     2.64   0.009     .0039754    .0274382
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.19
            Prob > F =    0.9028
.90281612

Linear regression                                      Number of obs =     505
                                                       F(  1,    81) =    1.19
                                                       Prob > F      =  0.2780
                                                       R-squared     =  0.0021
                                                       Root MSE      =  .11703

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0106406   .0097432     1.09   0.278    -.0087453    .0300266
       _cons |   .0082988   .0057703     1.44   0.154    -.0031824    .0197799
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     484
                                                       F(  1,    80) =    0.33
                                                       Prob > F      =  0.5662
                                                       R-squared     =  0.0014
                                                       Root MSE      =   .1108

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0081622   .0141703     0.58   0.566    -.0200376    .0363619
       _cons |   .0082988    .005771     1.44   0.154     -.003186    .0197835
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     478
                                                       F(  1,    78) =    1.14
                                                       Prob > F      =  0.2899
                                                       R-squared     =  0.0028
                                                       Root MSE      =  .12021

                                    (Std. Err. adjusted for 79 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0127983   .0120102     1.07   0.290    -.0111121    .0367087
       _cons |   .0082988    .005772     1.44   0.155    -.0031925      .01979
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     985
                                                       F(  3,   159) =    0.62
                                                       Prob > F      =  0.6011
                                                       R-squared     =  0.0014
                                                       Root MSE      =  .12658

                                   (Std. Err. adjusted for 160 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0106406   .0097192     1.09   0.275    -.0085548    .0298361
     hotline |   .0081622   .0141337     0.58   0.564    -.0197518    .0360761
     verdade |   .0127983   .0119771     1.07   0.287    -.0108564     .036453
       _cons |   .0082988   .0057561     1.44   0.151    -.0030696    .0196671
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   159) =    0.62
            Prob > F =    0.6011
.60107508


note: results saved to balance.xml

Linear regression                                      Number of obs =     780
                                                       F(  1,    81) =    0.18
                                                       Prob > F      =  0.6762
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .17971

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0064985   .0155032    -0.42   0.676    -.0373449     .024348
       _cons |   .0366492   .0120753     3.04   0.003     .0126231    .0606753
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     742
                                                       F(  1,    80) =    0.98
                                                       Prob > F      =  0.3255
                                                       R-squared     =  0.0018
                                                       Root MSE      =  .16969

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   -.014427   .0145822    -0.99   0.325    -.0434465    .0145925
       _cons |   .0366492   .0120766     3.03   0.003      .012616    .0606825
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     746
                                                       F(  1,    79) =    0.36
                                                       Prob > F      =  0.5497
                                                       R-squared     =  0.0007
                                                       Root MSE      =  .17663

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0091767   .0152728    -0.60   0.550    -.0395763     .021223
       _cons |   .0366492   .0120775     3.03   0.003     .0126095    .0606889
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1504
                                                       F(  3,   160) =    0.36
                                                       Prob > F      =  0.7848
                                                       R-squared     =  0.0009
                                                       Root MSE      =  .16867

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0064985   .0154619    -0.42   0.675    -.0370342    .0240373
     hotline |   -.014427   .0145418    -0.99   0.323    -.0431457    .0142917
     verdade |  -.0091767   .0152293    -0.60   0.548    -.0392531    .0208998
       _cons |   .0366492   .0120432     3.04   0.003     .0128651    .0604333
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.36
            Prob > F =    0.7848
.7848087

Linear regression                                      Number of obs =     505
                                                       F(  1,    81) =    0.03
                                                       Prob > F      =  0.8555
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .21317

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.004338   .0237519    -0.18   0.856    -.0515968    .0429208
       _cons |   .0497925   .0189381     2.63   0.010     .0121116    .0874734
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     484
                                                       F(  1,    80) =    0.88
                                                       Prob > F      =  0.3503
                                                       R-squared     =  0.0029
                                                       Root MSE      =  .19432

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0209859   .0223355    -0.94   0.350     -.065435    .0234631
       _cons |   .0497925   .0189404     2.63   0.010        .0121    .0874851
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     478
                                                       F(  1,    78) =    1.33
                                                       Prob > F      =  0.2525
                                                       R-squared     =  0.0041
                                                       Root MSE      =  .19037

                                    (Std. Err. adjusted for 79 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0244761   .0212304    -1.15   0.252    -.0667425    .0177903
       _cons |   .0497925   .0189436     2.63   0.010     .0120787    .0875064
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     985
                                                       F(  3,   159) =    0.77
                                                       Prob > F      =  0.5106
                                                       R-squared     =  0.0030
                                                       Root MSE      =  .19024

                                   (Std. Err. adjusted for 160 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.004338   .0236934    -0.18   0.855    -.0511323    .0424564
     hotline |  -.0209859   .0222778    -0.94   0.348    -.0649845    .0230126
     verdade |  -.0244761   .0211719    -1.16   0.249    -.0662905    .0173384
       _cons |   .0497925   .0188915     2.64   0.009      .012482    .0871031
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   159) =    0.77
            Prob > F =    0.5106
.51061524


note: results saved to balance.xml

Linear regression                                      Number of obs =     736
                                                       F(  1,    81) =    1.78
                                                       Prob > F      =  0.1862
                                                       R-squared     =  0.0027
                                                       Root MSE      =  .27571

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0285275   .0213955     1.33   0.186    -.0140428    .0710979
       _cons |    .902507   .0149462    60.38   0.000     .8727687    .9322453
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     705
                                                       F(  1,    80) =    1.17
                                                       Prob > F      =  0.2835
                                                       R-squared     =  0.0020
                                                       Root MSE      =  .27915

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0252387   .0233727     1.08   0.283    -.0212745    .0717519
       _cons |    .902507   .0149478    60.38   0.000     .8727599    .9322541
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     698
                                                       F(  1,    79) =    0.21
                                                       Prob > F      =  0.6483
                                                       R-squared     =  0.0004
                                                       Root MSE      =  .30454

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0116515   .0254435    -0.46   0.648    -.0622955    .0389925
       _cons |    .902507   .0149491    60.37   0.000     .8727516    .9322624
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1421
                                                       F(  3,   160) =    1.23
                                                       Prob > F      =  0.3004
                                                       R-squared     =  0.0036
                                                       Root MSE      =  .28108

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0285275    .021339     1.34   0.183     -.013615      .07067
     hotline |   .0252387   .0233086     1.08   0.281    -.0207934    .0712708
     verdade |  -.0116515   .0253715    -0.46   0.647    -.0617577    .0384547
       _cons |    .902507   .0149068    60.54   0.000     .8730676    .9319464
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.23
            Prob > F =    0.3004
.30037666

Linear regression                                      Number of obs =     474
                                                       F(  1,    81) =    0.05
                                                       Prob > F      =  0.8192
                                                       R-squared     =  0.0001
                                                       Root MSE      =   .2878

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .006521   .0284359     0.23   0.819    -.0500574    .0630994
       _cons |   .9058296   .0201055    45.05   0.000      .865826    .9458332
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     453
                                                       F(  1,    80) =    0.58
                                                       Prob > F      =  0.4504
                                                       R-squared     =  0.0020
                                                       Root MSE      =   .2742

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0246052    .032439     0.76   0.450    -.0399505    .0891609
       _cons |   .9058296    .020108    45.05   0.000     .8658134    .9458458
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     445
                                                       F(  1,    78) =    0.02
                                                       Prob > F      =  0.8827
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .28986

                                    (Std. Err. adjusted for 79 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0040803   .0275698     0.15   0.883     -.050807    .0589676
       _cons |   .9058296   .0201116    45.04   0.000     .8657905    .9458687
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     926
                                                       F(  3,   159) =    0.21
                                                       Prob > F      =  0.8900
                                                       R-squared     =  0.0011
                                                       Root MSE      =   .2798

                                   (Std. Err. adjusted for 160 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .006521   .0283667     0.23   0.818    -.0495032    .0625452
     hotline |   .0246052   .0323561     0.76   0.448     -.039298    .0885083
     verdade |   .0040803   .0274944     0.15   0.882    -.0502211    .0583817
       _cons |   .9058296   .0200566    45.16   0.000     .8662179    .9454413
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   159) =    0.21
            Prob > F =    0.8900
.88999795


note: results saved to balance.xml

Linear regression                                      Number of obs =     736
                                                       F(  1,    81) =    0.17
                                                       Prob > F      =  0.6844
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .12149

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0034505    .008459    -0.41   0.684    -.0202812    .0133802
       _cons |   .0167131   .0063005     2.65   0.010     .0041771    .0292491
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     705
                                                       F(  1,    80) =    0.00
                                                       Prob > F      =  0.9569
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .12953

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0006279   .0115894     0.05   0.957    -.0224358    .0236917
       _cons |   .0167131   .0063012     2.65   0.010     .0041734    .0292528
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     698
                                                       F(  1,    79) =    0.05
                                                       Prob > F      =  0.8241
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .12472

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0019638   .0088079    -0.22   0.824    -.0194955    .0155679
       _cons |   .0167131   .0063017     2.65   0.010     .0041698    .0292563
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1421
                                                       F(  3,   160) =    0.08
                                                       Prob > F      =  0.9727
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .12362

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0034505   .0084367    -0.41   0.683    -.0201121    .0132111
     hotline |   .0006279   .0115576     0.05   0.957    -.0221972    .0234531
     verdade |  -.0019638    .008783    -0.22   0.823    -.0193093    .0153817
       _cons |   .0167131   .0062839     2.66   0.009     .0043031    .0291231
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.08
            Prob > F =    0.9727
.9726837

Linear regression                                      Number of obs =     474
                                                       F(  1,    81) =    0.51
                                                       Prob > F      =  0.4787
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .11198

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0069676   .0097903     0.71   0.479     -.012512    .0264473
       _cons |   .0089686    .006215     1.44   0.153    -.0033973    .0213346
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     453
                                                       F(  1,    80) =    0.31
                                                       Prob > F      =  0.5768
                                                       R-squared     =  0.0014
                                                       Root MSE      =   .1145

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0084227   .0150299     0.56   0.577    -.0214878    .0383332
       _cons |   .0089686   .0062158     1.44   0.153    -.0034012    .0213385
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     445
                                                       F(  1,    78) =    0.22
                                                       Prob > F      =  0.6386
                                                       R-squared     =  0.0005
                                                       Root MSE      =  .10562

                                    (Std. Err. adjusted for 79 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0045449   .0096402     0.47   0.639    -.0146473    .0237371
       _cons |   .0089686   .0062169     1.44   0.153    -.0034083    .0213455
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     926
                                                       F(  3,   159) =    0.23
                                                       Prob > F      =  0.8786
                                                       R-squared     =  0.0007
                                                       Root MSE      =  .11786

                                   (Std. Err. adjusted for 160 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0069676   .0097665     0.71   0.477    -.0123212    .0262565
     hotline |   .0084227   .0149915     0.56   0.575    -.0211855    .0380309
     verdade |   .0045449   .0096139     0.47   0.637    -.0144424    .0235322
       _cons |   .0089686   .0061999     1.45   0.150    -.0032762    .0212134
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   159) =    0.23
            Prob > F =    0.8786
.87855198


note: results saved to balance.xml

. 
. foreach i in $votpast {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0, cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & drops2==0, cluster(ea)
 14.         estimates store `i'_10
 15.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & drops2==0, cluster(ea)
 16.         estimates store `i'_11
 17.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & drops2==0, cluster(ea)
 18.         estimates store `i'_12
 19.         regress `i' civiceduc hotline verdade if time==0 & drops2==0, cluster(ea)
 20.         test civiceduc hotline verdade
 21.         scalar define f`i'_4=r(p)
 22.         display f`i'_4
 23. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3" + " `i'_10" + " `i'_11" + " `i'
> _12"
 24.         xml_tab $list1, below save(balance.xml) append sheet("bal `i'")
 25.         estimates clear
 26. 
. }

Linear regression                                      Number of obs =     752
                                                       F(  1,    81) =    1.10
                                                       Prob > F      =  0.2968
                                                       R-squared     =  0.0018
                                                       Root MSE      =   .1958

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 turnout2004 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0163823   .0155994    -1.05   0.297    -.0474202    .0146556
       _cons |    .968254   .0085401   113.38   0.000     .9512619    .9852461
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     741
                                                       F(  1,    80) =    0.01
                                                       Prob > F      =  0.9143
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .17366

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 turnout2004 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |    .001443   .0133727     0.11   0.914    -.0251695    .0280555
       _cons |    .968254   .0085408   113.37   0.000     .9512572    .9852507
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     736
                                                       F(  1,    79) =    2.20
                                                       Prob > F      =  0.1423
                                                       R-squared     =  0.0028
                                                       Root MSE      =  .20085

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 turnout2004 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0213266   .0143883    -1.48   0.142    -.0499659    .0073127
       _cons |    .968254   .0085415   113.36   0.000     .9512525    .9852555
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1473
                                                       F(  3,   160) =    1.14
                                                       Prob > F      =  0.3365
                                                       R-squared     =  0.0025
                                                       Root MSE      =  .19769

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 turnout2004 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0163823   .0155579    -1.05   0.294    -.0471076    .0143429
     hotline |    .001443   .0133359     0.11   0.914    -.0248942    .0277802
     verdade |  -.0213266   .0143476    -1.49   0.139    -.0496617    .0070085
       _cons |    .968254   .0085173   113.68   0.000      .951433    .9850749
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.14
            Prob > F =    0.3365
.33654062

Linear regression                                      Number of obs =     490
                                                       F(  1,    81) =    0.95
                                                       Prob > F      =  0.3316
                                                       R-squared     =  0.0022
                                                       Root MSE      =  .20273

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 turnout2004 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0190138   .0194658    -0.98   0.332    -.0577446    .0197169
       _cons |    .966805   .0104812    92.24   0.000     .9459507    .9876593
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     492
                                                       F(  1,    80) =    0.02
                                                       Prob > F      =  0.8808
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .18301

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 turnout2004 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0026616   .0176973    -0.15   0.881    -.0378803    .0325572
       _cons |    .966805    .010482    92.24   0.000     .9459452    .9876648
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     476
                                                       F(  1,    79) =    0.64
                                                       Prob > F      =  0.4266
                                                       R-squared     =  0.0012
                                                       Root MSE      =  .19606

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 turnout2004 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0136135   .0170342    -0.80   0.427    -.0475191    .0202921
       _cons |    .966805   .0104831    92.22   0.000     .9459388    .9876712
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     976
                                                       F(  3,   160) =    0.44
                                                       Prob > F      =  0.7238
                                                       R-squared     =  0.0015
                                                       Root MSE      =  .20087

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 turnout2004 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0190138   .0194171    -0.98   0.329    -.0573607     .019333
     hotline |  -.0026616   .0176518    -0.15   0.880    -.0375221     .032199
     verdade |  -.0136135   .0169884    -0.80   0.424     -.047164     .019937
       _cons |    .966805    .010455    92.47   0.000     .9461573    .9874526
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.44
            Prob > F =    0.7238
.7238079


note: results saved to balance.xml

Linear regression                                      Number of obs =     819
                                                       F(  1,    81) =    0.00
                                                       Prob > F      =  0.9489
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .39412

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 guebas20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0019739   .0307166     0.06   0.949    -.0591425    .0630902
       _cons |   .8073171   .0210383    38.37   0.000     .7654575    .8491766
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     811
                                                       F(  1,    80) =    0.09
                                                       Prob > F      =  0.7662
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .39842

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 guebas20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0093121   .0312111    -0.30   0.766    -.0714242       .0528
       _cons |   .8073171     .02104    38.37   0.000     .7654461     .849188
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     798
                                                       F(  1,    79) =    0.12
                                                       Prob > F      =  0.7313
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .39895

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 guebas20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0109253   .0316987    -0.34   0.731    -.0740201    .0521694
       _cons |   .8073171   .0210419    38.37   0.000     .7654343    .8491999
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1608
                                                       F(  3,   160) =    0.08
                                                       Prob > F      =  0.9696
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .39829

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 guebas20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0019739   .0306338     0.06   0.949    -.0585249    .0624727
     hotline |  -.0093121   .0311245    -0.30   0.765    -.0707799    .0521557
     verdade |  -.0109253    .031608    -0.35   0.730    -.0733479    .0514973
       _cons |   .8073171   .0209816    38.48   0.000     .7658805    .8487537
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.08
            Prob > F =    0.9696
.96957359

Linear regression                                      Number of obs =     537
                                                       F(  1,    81) =    0.12
                                                       Prob > F      =  0.7336
                                                       R-squared     =  0.0002
                                                       Root MSE      =   .3973

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 guebas20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0120713   .0353408    -0.34   0.734    -.0823885    .0582459
       _cons |   .8106061   .0241177    33.61   0.000     .7626193    .8585928
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     537
                                                       F(  1,    80) =    0.00
                                                       Prob > F      =  0.9446
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .39151

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 guebas20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0025808   .0370452     0.07   0.945    -.0711415     .076303
       _cons |   .8106061   .0241196    33.61   0.000     .7626066    .8586055
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     522
                                                       F(  1,    79) =    0.01
                                                       Prob > F      =  0.9196
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .39125

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 guebas20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0033474   .0330765     0.10   0.920    -.0624897    .0691846
       _cons |   .8106061   .0241221    33.60   0.000     .7625922    .8586199
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1068
                                                       F(  3,   160) =    0.08
                                                       Prob > F      =  0.9709
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .39379

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 guebas20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0120713    .035251    -0.34   0.732    -.0816885     .057546
     hotline |   .0025808   .0369482     0.07   0.944    -.0703882    .0755498
     verdade |   .0033474   .0329864     0.10   0.919    -.0617975    .0684924
       _cons |   .8106061   .0240564    33.70   0.000      .763097    .8581151
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.08
            Prob > F =    0.9709
.97092456


note: results saved to balance.xml

Linear regression                                      Number of obs =     819
                                                       F(  1,    81) =    0.00
                                                       Prob > F      =  0.9964
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .12978

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
dlakha~20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0000417   .0091137     0.00   0.996    -.0180917    .0181752
       _cons |   .0170732   .0059404     2.87   0.005     .0052537    .0288927
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     811
                                                       F(  1,    80) =    0.07
                                                       Prob > F      =  0.7889
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .13489

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
dlakha~20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |    .002877   .0107078     0.27   0.789    -.0184323    .0241862
       _cons |   .0170732   .0059409     2.87   0.005     .0052505    .0288959
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     798
                                                       F(  1,    79) =    0.04
                                                       Prob > F      =  0.8463
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .12675

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
dlakha~20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0016093    .008275    -0.19   0.846    -.0180802    .0148616
       _cons |   .0170732   .0059414     2.87   0.005     .0052471    .0288992
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1608
                                                       F(  3,   160) =    0.06
                                                       Prob > F      =  0.9804
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .13096

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
dlakha~20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0000417   .0090892     0.00   0.996    -.0179085     .017992
     hotline |    .002877   .0106781     0.27   0.788    -.0182113    .0239652
     verdade |  -.0016093   .0082513    -0.20   0.846    -.0179047    .0146862
       _cons |   .0170732   .0059244     2.88   0.004     .0053731    .0287732
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.06
            Prob > F =    0.9804
.98036927

Linear regression                                      Number of obs =     537
                                                       F(  1,    81) =    1.02
                                                       Prob > F      =  0.3151
                                                       R-squared     =  0.0022
                                                       Root MSE      =  .11351

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
dlakha~20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0107393   .0106233     1.01   0.315    -.0103978    .0318763
       _cons |   .0075758   .0052911     1.43   0.156    -.0029518    .0181033
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     537
                                                       F(  1,    80) =    0.31
                                                       Prob > F      =  0.5784
                                                       R-squared     =  0.0011
                                                       Root MSE      =  .10525

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
dlakha~20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0070763   .0126799     0.56   0.578    -.0181576    .0323101
       _cons |   .0075758   .0052915     1.43   0.156    -.0029546    .0181061
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     522
                                                       F(  1,    79) =    0.24
                                                       Prob > F      =  0.6269
                                                       R-squared     =  0.0004
                                                       Root MSE      =  .09757

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
dlakha~20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0040521   .0083047     0.49   0.627     -.012478    .0205823
       _cons |   .0075758    .005292     1.43   0.156    -.0029578    .0181093
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1068
                                                       F(  3,   160) =    0.39
                                                       Prob > F      =  0.7616
                                                       R-squared     =  0.0012
                                                       Root MSE      =  .11388

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
dlakha~20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0107393   .0105963     1.01   0.312    -.0101874    .0316659
     hotline |   .0070763   .0126467     0.56   0.577    -.0178998    .0320523
     verdade |   .0040521   .0082821     0.49   0.625    -.0123042    .0204085
       _cons |   .0075758   .0052776     1.44   0.153     -.002847    .0179985
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.39
            Prob > F =    0.7616
.76159473


note: results saved to balance.xml

Linear regression                                      Number of obs =     822
                                                       F(  1,    81) =    0.21
                                                       Prob > F      =  0.6459
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .40632

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
frelimo20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0150074   .0325426     0.46   0.646    -.0497421    .0797568
       _cons |   .7845036   .0226803    34.59   0.000      .739377    .8296303
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     813
                                                       F(  1,    80) =    0.00
                                                       Prob > F      =  0.9881
                                                       R-squared     =  0.0000
                                                       Root MSE      =   .4115

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
frelimo20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0004964   .0331563     0.01   0.988    -.0654868    .0664796
       _cons |   .7845036   .0226822    34.59   0.000     .7393647    .8296426
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     800
                                                       F(  1,    79) =    0.00
                                                       Prob > F      =  0.9749
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .41134

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
frelimo20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0010261   .0324735     0.03   0.975    -.0636109     .065663
       _cons |   .7845036   .0226842    34.58   0.000     .7393519    .8296553
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1609
                                                       F(  3,   160) =    0.10
                                                       Prob > F      =  0.9613
                                                       R-squared     =  0.0002
                                                       Root MSE      =   .4087

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
frelimo20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0150074    .032455     0.46   0.644     -.049088    .0791027
     hotline |   .0004964   .0330643     0.02   0.988    -.0648025    .0657952
     verdade |   .0010261   .0323806     0.03   0.975    -.0629224    .0649745
       _cons |   .7845036   .0226192    34.68   0.000     .7398329    .8291744
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.10
            Prob > F =    0.9613
.96133051

Linear regression                                      Number of obs =     538
                                                       F(  1,    81) =    0.03
                                                       Prob > F      =  0.8653
                                                       R-squared     =  0.0001
                                                       Root MSE      =   .4094

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
frelimo20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0063031   .0370336     0.17   0.865    -.0673821    .0799884
       _cons |   .7849057   .0264713    29.65   0.000     .7322361    .8375753
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     536
                                                       F(  1,    80) =    0.10
                                                       Prob > F      =  0.7492
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .40728

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
frelimo20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0121423   .0378483     0.32   0.749    -.0631781    .0874627
       _cons |   .7849057   .0264734    29.65   0.000     .7322219    .8375894
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     522
                                                       F(  1,    79) =    0.22
                                                       Prob > F      =  0.6382
                                                       R-squared     =  0.0004
                                                       Root MSE      =  .40577

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
frelimo20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0166508   .0352698     0.47   0.638    -.0535519    .0868535
       _cons |   .7849057   .0264762    29.65   0.000     .7322062    .8376051
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1066
                                                       F(  3,   160) =    0.08
                                                       Prob > F      =  0.9693
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .40542

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
frelimo20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0063031   .0369396     0.17   0.865    -.0666489    .0792552
     hotline |   .0121423   .0377492     0.32   0.748    -.0624086    .0866932
     verdade |   .0166508   .0351738     0.47   0.637     -.052814    .0861156
       _cons |   .7849057   .0264041    29.73   0.000     .7327601    .8370512
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.08
            Prob > F =    0.9693
.96927422


note: results saved to balance.xml

Linear regression                                      Number of obs =     822
                                                       F(  1,    81) =    0.07
                                                       Prob > F      =  0.7975
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .12491

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 renamo20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0022792   .0088547    -0.26   0.798    -.0198973    .0153388
       _cons |   .0169492   .0058882     2.88   0.005     .0052335    .0286648
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     813
                                                       F(  1,    80) =    0.08
                                                       Prob > F      =  0.7764
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .13473

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 renamo20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0030508   .0107066     0.28   0.776    -.0182559    .0243576
       _cons |   .0169492   .0058887     2.88   0.005     .0052303     .028668
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     800
                                                       F(  1,    79) =    0.03
                                                       Prob > F      =  0.8615
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .12659

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 renamo20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0014453   .0082585    -0.18   0.862    -.0178834    .0149928
       _cons |   .0169492   .0058892     2.88   0.005      .005227    .0286713
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1609
                                                       F(  3,   160) =    0.09
                                                       Prob > F      =  0.9665
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .12859

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 renamo20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0022792   .0088308    -0.26   0.797    -.0197193    .0151608
     hotline |   .0030508   .0106769     0.29   0.775    -.0180349    .0241366
     verdade |  -.0014453   .0082348    -0.18   0.861    -.0177083    .0148177
       _cons |   .0169492   .0058723     2.89   0.004     .0053519    .0285465
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.09
            Prob > F =    0.9665
.96648253

Linear regression                                      Number of obs =     538
                                                       F(  1,    81) =    1.03
                                                       Prob > F      =  0.3133
                                                       R-squared     =  0.0023
                                                       Root MSE      =   .1134

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 renamo20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0107678   .0106132     1.01   0.313     -.010349    .0318847
       _cons |   .0075472   .0052707     1.43   0.156    -.0029399    .0180342
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     536
                                                       F(  1,    80) =    0.68
                                                       Prob > F      =  0.4133
                                                       R-squared     =  0.0023
                                                       Root MSE      =  .11361

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 renamo20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |    .010903   .0132576     0.82   0.413    -.0154805    .0372866
       _cons |   .0075472   .0052711     1.43   0.156    -.0029427    .0180371
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     522
                                                       F(  1,    79) =    0.25
                                                       Prob > F      =  0.6209
                                                       R-squared     =  0.0004
                                                       Root MSE      =  .09757

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 renamo20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |    .004126   .0083086     0.50   0.621    -.0124119    .0206639
       _cons |   .0075472   .0052717     1.43   0.156    -.0029459    .0180402
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1066
                                                       F(  3,   160) =    0.48
                                                       Prob > F      =  0.6960
                                                       R-squared     =  0.0015
                                                       Root MSE      =  .11792

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 renamo20042 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0107678   .0105862     1.02   0.311    -.0101389    .0316746
     hotline |    .010903   .0132229     0.82   0.411     -.015211     .037017
     verdade |    .004126    .008286     0.50   0.619    -.0122381    .0204901
       _cons |   .0075472   .0052573     1.44   0.153    -.0028356    .0179299
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.48
            Prob > F =    0.6960
.69599811


note: results saved to balance.xml

. 
. foreach i in $survey {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0, cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & drops2==0, cluster(ea)
 14.         estimates store `i'_10
 15.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & drops2==0, cluster(ea)
 16.         estimates store `i'_11
 17.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & drops2==0, cluster(ea)
 18.         estimates store `i'_12
 19.         regress `i' civiceduc hotline verdade if time==0 & drops2==0, cluster(ea)
 20.         test civiceduc hotline verdade
 21.         scalar define f`i'_4=r(p)
 22.         display f`i'_4
 23. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3" + " `i'_10" + " `i'_11" + " `i'
> _12"
 24.         xml_tab $list1, below save(balance.xml) append sheet("bal `i'")
 25.         estimates clear
 26. 
. }

Linear regression                                      Number of obs =     756
                                                       F(  1,    81) =    0.05
                                                       Prob > F      =  0.8315
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .96989

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     zsctcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0203051   .0951425     0.21   0.832    -.1689987    .2096089
       _cons |  -1.05e-07   .0727061    -0.00   1.000    -.1446624    .1446622
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     765
                                                       F(  1,    80) =    0.33
                                                       Prob > F      =  0.5646
                                                       R-squared     =  0.0009
                                                       Root MSE      =  .97373

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     zsctcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0597507   .1033042    -0.58   0.565    -.2653327    .1458313
       _cons |  -1.05e-07   .0727111    -0.00   1.000    -.1446997    .1446995
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     743
                                                       F(  1,    79) =    0.01
                                                       Prob > F      =  0.9155
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .96904

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     zsctcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0103827   .0975118     0.11   0.915    -.1837096    .2044751
       _cons |  -1.05e-07   .0727181    -0.00   1.000     -.144742    .1447417
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1490
                                                       F(  3,   160) =    0.26
                                                       Prob > F      =  0.8517
                                                       R-squared     =  0.0011
                                                       Root MSE      =  .95545

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     zsctcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0203051   .0948885     0.21   0.831    -.1670903    .2077004
     hotline |  -.0597507   .1030213    -0.58   0.563    -.2632077    .1437062
     verdade |   .0103827   .0972352     0.11   0.915    -.1816473    .2024128
       _cons |  -1.05e-07   .0725119    -0.00   1.000     -.143204    .1432038
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.26
            Prob > F =    0.8517
.85165586

Linear regression                                      Number of obs =     496
                                                       F(  1,    81) =    0.02
                                                       Prob > F      =  0.8862
                                                       R-squared     =  0.0001
                                                       Root MSE      =  1.0094

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     zsctcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0162734   .1133211     0.14   0.886       -.2092    .2417468
       _cons |  -.0382377   .0860504    -0.44   0.658     -.209451    .1329756
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     510
                                                       F(  1,    80) =    0.22
                                                       Prob > F      =  0.6420
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .98606

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     zsctcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0558198   .1196038    -0.47   0.642    -.2938389    .1821994
       _cons |  -.0382377   .0860545    -0.44   0.658    -.2094917    .1330163
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     481
                                                       F(  1,    79) =    0.70
                                                       Prob > F      =  0.4065
                                                       R-squared     =  0.0024
                                                       Root MSE      =  .96884

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     zsctcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0950845   .1139471     0.83   0.407    -.1317215    .3218905
       _cons |  -.0382377   .0860664    -0.44   0.658    -.2095486    .1330731
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     983
                                                       F(  3,   160) =    0.64
                                                       Prob > F      =  0.5926
                                                       R-squared     =  0.0030
                                                       Root MSE      =  .97192

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     zsctcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0162734    .113038     0.14   0.886    -.2069656    .2395123
     hotline |  -.0558198   .1192993    -0.47   0.640    -.2914241    .1797846
     verdade |   .0950845   .1136413     0.84   0.404    -.1293459    .3195149
       _cons |  -.0382377   .0858354    -0.45   0.657    -.2077542    .1312788
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.64
            Prob > F =    0.5926
.59256296


note: results saved to balance.xml

Linear regression                                      Number of obs =     719
                                                       F(  1,    81) =    0.39
                                                       Prob > F      =  0.5364
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .97976

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      zsccne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .056843   .0915531     0.62   0.536    -.1253189    .2390049
       _cons |   1.13e-07   .0658733     0.00   1.000    -.1310671    .1310673
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     728
                                                       F(  1,    80) =    0.45
                                                       Prob > F      =  0.5023
                                                       R-squared     =  0.0011
                                                       Root MSE      =  .96519

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      zsccne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0640555   .0950469     0.67   0.502    -.1250939    .2532048
       _cons |   1.13e-07   .0658777     0.00   1.000    -.1311008     .131101
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     705
                                                       F(  1,    79) =    0.69
                                                       Prob > F      =  0.4085
                                                       R-squared     =  0.0017
                                                       Root MSE      =  .96632

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      zsccne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0793518   .0954863     0.83   0.408     -.110709    .2694125
       _cons |   1.13e-07   .0658844     0.00   1.000    -.1311395    .1311397
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1414
                                                       F(  3,   160) =    0.27
                                                       Prob > F      =  0.8460
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .95466

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      zsccne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .056843   .0913104     0.62   0.534    -.1234861    .2371721
     hotline |   .0640555   .0947886     0.68   0.500    -.1231426    .2512536
     verdade |   .0793518   .0952172     0.83   0.406    -.1086929    .2673964
       _cons |   1.13e-07   .0656987     0.00   1.000    -.1297484    .1297486
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.27
            Prob > F =    0.8460
.8460033

Linear regression                                      Number of obs =     478
                                                       F(  1,    81) =    0.72
                                                       Prob > F      =  0.4001
                                                       R-squared     =  0.0021
                                                       Root MSE      =  .98055

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      zsccne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0907361   .1072707     0.85   0.400    -.1226989    .3041711
       _cons |   -.020374   .0770299    -0.26   0.792    -.1736394    .1328914
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     486
                                                       F(  1,    80) =    0.97
                                                       Prob > F      =  0.3287
                                                       R-squared     =  0.0032
                                                       Root MSE      =  .94731

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      zsccne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .1064895   .1083606     0.98   0.329    -.1091549     .322134
       _cons |   -.020374   .0770345    -0.26   0.792    -.1736774    .1329295
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     456
                                                       F(  1,    79) =    2.28
                                                       Prob > F      =  0.1352
                                                       R-squared     =  0.0068
                                                       Root MSE      =  .94999

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      zsccne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .1573605   .1042585     1.51   0.135    -.0501609     .364882
       _cons |   -.020374   .0770457    -0.26   0.792    -.1737297    .1329817
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     938
                                                       F(  3,   160) =    0.78
                                                       Prob > F      =  0.5066
                                                       R-squared     =  0.0036
                                                       Root MSE      =  .94166

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      zsccne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0907361   .1070065     0.85   0.398    -.1205913    .3020634
     hotline |   .1064895   .1080874     0.99   0.326    -.1069724    .3199515
     verdade |   .1573605   .1039805     1.51   0.132    -.0479906    .3627117
       _cons |   -.020374   .0768402    -0.27   0.791    -.1721259    .1313779
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.78
            Prob > F =    0.5066
.50662665


note: results saved to balance.xml

Linear regression                                      Number of obs =     759
                                                       F(  1,    81) =    0.27
                                                       Prob > F      =  0.6062
                                                       R-squared     =  0.0007
                                                       Root MSE      =  .99165

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      zscff4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0535131   .1034034    -0.52   0.606    -.2592534    .1522272
       _cons |   1.18e-07   .0782872     0.00   1.000    -.1557668     .155767
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     737
                                                       F(  1,    80) =    0.12
                                                       Prob > F      =  0.7303
                                                       R-squared     =  0.0004
                                                       Root MSE      =  1.0136

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      zscff4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0391815   .1132839     0.35   0.730    -.1862606    .2646236
       _cons |   1.18e-07   .0782947     0.00   1.000    -.1558112    .1558115
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     735
                                                       F(  1,    79) =    0.03
                                                       Prob > F      =  0.8655
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .98546

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      zscff4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0179652   .1057073     0.17   0.865      -.19244    .2283703
       _cons |   1.18e-07   .0783009     0.00   1.000     -.155854    .1558542
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1483
                                                       F(  3,   160) =    0.31
                                                       Prob > F      =  0.8210
                                                       R-squared     =  0.0012
                                                       Root MSE      =   .9954

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      zscff4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0535131    .103128    -0.52   0.605    -.2571808    .1501545
     hotline |   .0391815   .1129713     0.35   0.729    -.1839258    .2622888
     verdade |   .0179652   .1054073     0.17   0.865    -.1902038    .2261341
       _cons |   1.18e-07   .0780787     0.00   1.000    -.1541976    .1541978
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.31
            Prob > F =    0.8210
.82099084

Linear regression                                      Number of obs =     498
                                                       F(  1,    81) =    0.11
                                                       Prob > F      =  0.7416
                                                       R-squared     =  0.0004
                                                       Root MSE      =  1.0069

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      zscff4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0411711   .1244321    -0.33   0.742     -.288752    .2064099
       _cons |  -.0069083   .0903552    -0.08   0.939    -.1866868    .1728702
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     491
                                                       F(  1,    80) =    0.44
                                                       Prob > F      =  0.5106
                                                       R-squared     =  0.0017
                                                       Root MSE      =  1.0485

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      zscff4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0859729   .1301002     0.66   0.511    -.1729347    .3448805
       _cons |  -.0069083   .0903634    -0.08   0.939    -.1867372    .1729205
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     477
                                                       F(  1,    79) =    0.12
                                                       Prob > F      =  0.7263
                                                       R-squared     =  0.0005
                                                       Root MSE      =  .95798

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      zscff4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0409833   .1166624    -0.35   0.726     -.273194    .1912274
       _cons |  -.0069083   .0903732    -0.08   0.939    -.1867916     .172975
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     986
                                                       F(  3,   160) =    0.46
                                                       Prob > F      =  0.7127
                                                       R-squared     =  0.0027
                                                       Root MSE      =  1.0069

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      zscff4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0411711   .1241212    -0.33   0.741    -.2862983    .2039562
     hotline |   .0859729   .1297634     0.66   0.509    -.1702969    .3422428
     verdade |  -.0409833   .1163478    -0.35   0.725    -.2707587    .1887921
       _cons |  -.0069083   .0901294    -0.08   0.939    -.1849051    .1710884
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.46
            Prob > F =    0.7127
.71272962


note: results saved to balance.xml

Linear regression                                      Number of obs =     709
                                                       F(  1,    81) =    0.68
                                                       Prob > F      =  0.4127
                                                       R-squared     =  0.0012
                                                       Root MSE      =  .94963

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zsccount |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0665417   .0808148    -0.82   0.413    -.2273377    .0942543
       _cons |   8.34e-08    .059848     0.00   1.000    -.1190786    .1190788
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     691
                                                       F(  1,    80) =    0.21
                                                       Prob > F      =  0.6493
                                                       R-squared     =  0.0004
                                                       Root MSE      =   .9824

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zsccount |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0410441   .0899176     0.46   0.649    -.1378976    .2199858
       _cons |   8.34e-08   .0598536     0.00   1.000    -.1191125    .1191126
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     680
                                                       F(  1,    79) =    0.35
                                                       Prob > F      =  0.5551
                                                       R-squared     =  0.0007
                                                       Root MSE      =  .93121

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zsccount |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0506399   .0854427    -0.59   0.555    -.2207092    .1194295
       _cons |   8.34e-08    .059859     0.00   1.000    -.1191463    .1191465
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1380
                                                       F(  3,   160) =    0.64
                                                       Prob > F      =  0.5914
                                                       R-squared     =  0.0021
                                                       Root MSE      =  .93092

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zsccount |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0665417   .0806019    -0.83   0.410    -.2257225    .0926391
     hotline |   .0410441   .0896722     0.46   0.648    -.1360497    .2181379
     verdade |  -.0506399   .0852019    -0.59   0.553    -.2189052    .1176254
       _cons |   8.34e-08   .0596903     0.00   1.000    -.1178824    .1178826
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.64
            Prob > F =    0.5914
.59136437

Linear regression                                      Number of obs =     455
                                                       F(  1,    81) =    0.56
                                                       Prob > F      =  0.4569
                                                       R-squared     =  0.0017
                                                       Root MSE      =  .98193

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zsccount |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0805711   .1077818    -0.75   0.457    -.2950231    .1338809
       _cons |   .0409466   .0808682     0.51   0.614    -.1199557    .2018489
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     453
                                                       F(  1,    80) =    0.30
                                                       Prob > F      =  0.5863
                                                       R-squared     =  0.0011
                                                       Root MSE      =  .96574

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zsccount |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0629127   .1151461    -0.55   0.586    -.2920607    .1662354
       _cons |   .0409466   .0808747     0.51   0.614    -.1199992    .2018924
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     435
                                                       F(  1,    79) =    1.40
                                                       Prob > F      =  0.2399
                                                       R-squared     =  0.0046
                                                       Root MSE      =  .94486

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zsccount |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.1281387    .108216    -1.18   0.240    -.3435372    .0872599
       _cons |   .0409466   .0808848     0.51   0.614    -.1200505    .2019437
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     897
                                                       F(  3,   160) =    0.48
                                                       Prob > F      =  0.6975
                                                       R-squared     =  0.0024
                                                       Root MSE      =  .93344

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zsccount |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0805711   .1075185    -0.75   0.455    -.2929097    .1317674
     hotline |  -.0629127   .1148556    -0.55   0.585    -.2897411    .1639157
     verdade |  -.1281387   .1079295    -1.19   0.237    -.3412888    .0850115
       _cons |   .0409466   .0806706     0.51   0.612      -.11837    .2002632
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.48
            Prob > F =    0.6975
.69746969


note: results saved to balance.xml

Linear regression                                      Number of obs =     664
                                                       F(  1,    81) =    0.42
                                                       Prob > F      =  0.5179
                                                       R-squared     =  0.0006
                                                       Root MSE      =  1.0124

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zscff9_3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.048149   .0741329    -0.65   0.518    -.1956502    .0993522
       _cons |  -3.28e-08   .0534372    -0.00   1.000    -.1063234    .1063233
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     654
                                                       F(  1,    80) =    0.21
                                                       Prob > F      =  0.6483
                                                       R-squared     =  0.0003
                                                       Root MSE      =       1

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zscff9_3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0326944   .0714152    -0.46   0.648    -.1748153    .1094265
       _cons |  -3.28e-08   .0534419    -0.00   1.000    -.1063529    .1063528
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     638
                                                       F(  1,    79) =    1.49
                                                       Prob > F      =  0.2255
                                                       R-squared     =  0.0025
                                                       Root MSE      =  .96924

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zscff9_3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0966535   .0791201    -1.22   0.225    -.2541382    .0608312
       _cons |  -3.28e-08   .0534471    -0.00   1.000    -.1063839    .1063838
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1302
                                                       F(  3,   160) =    0.52
                                                       Prob > F      =  0.6699
                                                       R-squared     =  0.0012
                                                       Root MSE      =  .99153

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zscff9_3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.048149   .0739389    -0.65   0.516    -.1941711     .097873
     hotline |  -.0326944   .0712221    -0.46   0.647    -.1733511    .1079623
     verdade |  -.0966535   .0788985    -1.23   0.222    -.2524702    .0591633
       _cons |  -3.28e-08   .0532974    -0.00   1.000    -.1052572    .1052571
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.52
            Prob > F =    0.6699
.66988016

Linear regression                                      Number of obs =     426
                                                       F(  1,    81) =    0.09
                                                       Prob > F      =  0.7608
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .99197

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zscff9_3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0304933   .0998304    -0.31   0.761    -.2291246    .1681379
       _cons |   -.014831    .069907    -0.21   0.833     -.153924    .1242621
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     429
                                                       F(  1,    80) =    0.05
                                                       Prob > F      =  0.8313
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .98082

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zscff9_3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0195424   .0914309    -0.21   0.831    -.2014956    .1624109
       _cons |   -.014831   .0699118    -0.21   0.833    -.1539598    .1242979
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     405
                                                       F(  1,    79) =    0.81
                                                       Prob > F      =  0.3701
                                                       R-squared     =  0.0025
                                                       Root MSE      =  .95412

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zscff9_3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0948439   .1052247    -0.90   0.370    -.3042883    .1146006
       _cons |   -.014831   .0699221    -0.21   0.833    -.1540074    .1243455
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     846
                                                       F(  3,   160) =    0.30
                                                       Prob > F      =  0.8259
                                                       R-squared     =  0.0012
                                                       Root MSE      =  .98474

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zscff9_3 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0304933   .0995892    -0.31   0.760    -.2271722    .1661855
     hotline |  -.0195424   .0912038    -0.21   0.831    -.1996608    .1605761
     verdade |  -.0948439   .1049478    -0.90   0.368    -.3021054    .1124176
       _cons |   -.014831   .0697381    -0.21   0.832    -.1525569    .1228949
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.30
            Prob > F =    0.8259
.82585604


note: results saved to balance.xml

Linear regression                                      Number of obs =     717
                                                       F(  1,    81) =    0.32
                                                       Prob > F      =  0.5715
                                                       R-squared     =  0.0005
                                                       Root MSE      =  1.0219

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zscff9_4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0442064   .0778115    -0.57   0.572    -.1990269    .1106141
       _cons |  -5.22e-08   .0510059    -0.00   1.000    -.1014859    .1014857
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     713
                                                       F(  1,    80) =    0.01
                                                       Prob > F      =  0.9109
                                                       R-squared     =  0.0000
                                                       Root MSE      =  1.0212

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zscff9_4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0089083   .0793777     0.11   0.911    -.1490583    .1668749
       _cons |  -5.22e-08     .05101    -0.00   1.000    -.1015132    .1015131
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     684
                                                       F(  1,    79) =    1.54
                                                       Prob > F      =  0.2181
                                                       R-squared     =  0.0027
                                                       Root MSE      =  .96072

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zscff9_4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.1005886   .0810141    -1.24   0.218    -.2618431    .0606658
       _cons |  -5.22e-08   .0510155    -0.00   1.000    -.1015439    .1015438
------------------------------------------------------------------------------

Linear regression                                      Number of obs =    1410
                                                       F(  3,   160) =    0.69
                                                       Prob > F      =  0.5567
                                                       R-squared     =  0.0018
                                                       Root MSE      =  1.0034

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zscff9_4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0442064   .0776054    -0.57   0.570    -.1974693    .1090566
     hotline |   .0089083    .079161     0.11   0.911     -.147427    .1652435
     verdade |  -.1005886   .0807842    -1.25   0.215    -.2601295    .0589522
       _cons |  -5.22e-08   .0508708    -0.00   1.000    -.1004649    .1004648
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.69
            Prob > F =    0.5567
.55672766

Linear regression                                      Number of obs =     470
                                                       F(  1,    81) =    0.11
                                                       Prob > F      =  0.7450
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .99822

                                    (Std. Err. adjusted for 82 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zscff9_4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0319433   .0978842    -0.33   0.745    -.2267022    .1628156
       _cons |  -.0169719   .0723031    -0.23   0.815    -.1608324    .1268886
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     471
                                                       F(  1,    80) =    0.12
                                                       Prob > F      =  0.7247
                                                       R-squared     =  0.0003
                                                       Root MSE      =   .9822

                                    (Std. Err. adjusted for 81 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zscff9_4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0347379   .0982791    -0.35   0.725    -.2303195    .1608437
       _cons |  -.0169719   .0723085    -0.23   0.815    -.1608703    .1269265
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     437
                                                       F(  1,    79) =    0.47
                                                       Prob > F      =  0.4942
                                                       R-squared     =  0.0013
                                                       Root MSE      =  .94647

                                    (Std. Err. adjusted for 80 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zscff9_4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0693829   .1010235    -0.69   0.494    -.2704652    .1316993
       _cons |  -.0169719   .0723201    -0.23   0.815    -.1609215    .1269777
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     928
                                                       F(  3,   160) =    0.16
                                                       Prob > F      =  0.9239
                                                       R-squared     =  0.0006
                                                       Root MSE      =  .98966

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zscff9_4 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0319433   .0976431    -0.33   0.744    -.2247788    .1608923
     hotline |  -.0347379   .0980297    -0.35   0.724     -.228337    .1588612
     verdade |  -.0693829    .100751    -0.69   0.492    -.2683561    .1295903
       _cons |  -.0169719    .072125    -0.24   0.814    -.1594117    .1254679
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.16
            Prob > F =    0.9239
.92392116


note: results saved to balance.xml

. global votint="turnoutresp guebas2 dlakhama2 simango2 frelimo2 renamo2"

. global votpast="turnout2004 guebas20042 dlakhama20042 frelimo20042 renamo20042"

. global survey="zsctcne zsccne zscff4 zsccount zscff9_3 zscff9_4"

. 
. global list2=""

. matrix define fpvalue_1=(fturnoutresp_1 \ fguebas2_1 \ fdlakhama2_1 \ fsimango2_1 \ ffrelimo2_
> 1 \ frenamo2_1 \ fturnout2004_1 \ fguebas20042_1 \ fdlakhama20042_1 \ ffrelimo20042_1 \ frenam
> o20042_1 \ fzsctcne_1 \ fzsccne_1 \ fzscff4_1 \ fzsccount_1 \ fzscff9_3_1 \ fzscff9_4_1)

. matrix rownames fpvalue_1 = "turnoutresp" "guebas2" "dlakhama2" "simango2" "frelimo2" "renamo2
> " "turnout2004" "guebas20042" "dlakhama20042" "frelimo20042" "renamo20042" "zsctcne" "zsccne" 
> "zscff4" "zsccount" "zscff9_3" "zscff9_4"

. matrix define fpvalue_4=(fturnoutresp_4 \ fguebas2_4 \ fdlakhama2_4 \ fsimango2_4 \ ffrelimo2_
> 4 \ frenamo2_4 \ fturnout2004_4 \ fguebas20042_4 \ fdlakhama20042_4 \ ffrelimo20042_4 \ frenam
> o20042_4 \ fzsctcne_4 \ fzsccne_4 \ fzscff4_4 \ fzsccount_4 \ fzscff9_3_4 \ fzscff9_4_4)

. matrix fpvalue= (fpvalue_1, fpvalue_4)

. global list2="$list2" + " fpvalue"

. xml_tab $list2, save(balance.xml) append sheet("fpvalue baseout") 


note: results saved to balance.xml

. estimates clear

. 
. *******************************************************************
. *****  TABLE 1 AND OA TABLE 4: REGRESSIONS OF BALLOT RESULTS  *****
. *******************************************************************
. 
. global out1="bsturnoutpres09 bsturnoutparl09" 

. global out2="bsnullpres09 bsnullparl09"

. global out3="bsblankpres09 bsblankparl09"

. global out4="bsguebas09 bsdhlakama09 bssimango09"

. global out5="bsfrelimo09 bsrenamo09"

. 
. global ea="count2009 policesta policesta_miss sewer sewer_miss recreation recreation_miss road
>  road_miss"

. 
. global list1=""

. global list2=""

. 
. foreach i in $out1 {
  2. 
.         regress `i' $treat $prov if v==1 & time==1
  3.         estimates store `i'_2
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2=r(mean)
  6.         display m_`i'_2
  7.         test civiceduc = hotline
  8.         scalar define t1_`i'_2=r(p)
  9.         display t1_`i'_2
 10.         test civiceduc = verdade
 11.         scalar define t2_`i'_2=r(p)
 12.         display t2_`i'_2
 13.         test hotline = verdade
 14.         scalar define t3_`i'_2=r(p)
 15.         display t3_`i'_2
 16.         test civiceduc hotline verdade
 17.         scalar define t4_`i'_2=r(p)
 18.         display t4_`i'_2
 19.         
.         regress `i' $treat $prov $ea if v==1 & time==1
 20.         estimates store `i'_3
 21.         sum `i' if e(sample) & control == 1
 22.         scalar define m_`i'_3=r(mean)
 23.         display m_`i'_3
 24.         test civiceduc = hotline
 25.         scalar define t1_`i'_3=r(p)
 26.         display t1_`i'_3
 27.         test civiceduc = verdade
 28.         scalar define t2_`i'_3=r(p)
 29.         display t2_`i'_3
 30.         test hotline = verdade
 31.         scalar define t3_`i'_3=r(p)
 32.         display t3_`i'_3
 33.         test civiceduc hotline verdade
 34.         scalar define t4_`i'_3=r(p)
 35.         display t4_`i'_3
 36.         
.         global list1="$list1" + " `i'_2" + " `i'_3"
 37. 
. }

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  6,   154) =   16.83
       Model |  1.27192419     6  .211987365           Prob > F      =  0.0000
    Residual |  1.93942964   154  .012593699           R-squared     =  0.3961
-------------+------------------------------           Adj R-squared =  0.3725
       Total |  3.21135383   160  .020070961           Root MSE      =  .11222

------------------------------------------------------------------------------
bsturnou~s09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0471422   .0247931     1.90   0.059    -.0018363    .0961206
     hotline |   .0487281   .0249428     1.95   0.053    -.0005461    .0980023
     verdade |   .0468717   .0251053     1.87   0.064    -.0027235    .0964669
         pr1 |  -.1010858   .0249428    -4.05   0.000      -.15036   -.0518116
         pr2 |   .0050453   .0249428     0.20   0.840    -.0442289    .0543196
         pr3 |   .1426151   .0249506     5.72   0.000     .0933255    .1919048
       _cons |   .4286921   .0229761    18.66   0.000     .3833031    .4740812
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsturnou~s09 |        41    .4400518    .1197301   .2245571     .73734
.44005181

 ( 1)  civiceduc - hotline = 0

       F(  1,   154) =    0.00
            Prob > F =    0.9494
.94938402

 ( 1)  civiceduc - verdade = 0

       F(  1,   154) =    0.00
            Prob > F =    0.9914
.99142146

 ( 1)  hotline - verdade = 0

       F(  1,   154) =    0.01
            Prob > F =    0.9415
.9415027

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   154) =    1.83
            Prob > F =    0.1435
.14349852

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 15,   145) =    7.63
       Model |  1.41693971    15  .094462648           Prob > F      =  0.0000
    Residual |  1.79441411   145   .01237527           R-squared     =  0.4412
-------------+------------------------------           Adj R-squared =  0.3834
       Total |  3.21135383   160  .020070961           Root MSE      =  .11124

---------------------------------------------------------------------------------
bsturnoutpres09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      civiceduc |   .0533011   .0248756     2.14   0.034     .0041355    .1024667
        hotline |   .0529413   .0249628     2.12   0.036     .0036033    .1022793
        verdade |   .0539214   .0253623     2.13   0.035     .0037939    .1040489
            pr1 |   -.070979   .0272316    -2.61   0.010    -.1248012   -.0171569
            pr2 |   .0337753   .0303834     1.11   0.268    -.0262763     .093827
            pr3 |   .1408417   .0270852     5.20   0.000     .0873089    .1943744
      count2009 |  -.0093354    .003845    -2.43   0.016     -.016935   -.0017358
      policesta |   .0188747   .0197196     0.96   0.340    -.0201003    .0578497
 policesta_miss |  -.0514558   .1176932    -0.44   0.663    -.2840718    .1811601
          sewer |   .0052563   .0303237     0.17   0.863    -.0546772    .0651897
     sewer_miss |   .2148039    .181412     1.18   0.238    -.1437496    .5733574
     recreation |   .0342175   .0242991     1.41   0.161    -.0138086    .0822436
recreation_miss |  -.0129363   .0545322    -0.24   0.813     -.120717    .0948444
           road |   .0308625   .0240276     1.28   0.201     -.016627     .078352
      road_miss |  -.0667758   .1149113    -0.58   0.562    -.2938934    .1603417
          _cons |   .4096682    .033452    12.25   0.000     .3435518    .4757846
---------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsturnou~s09 |        41    .4400518    .1197301   .2245571     .73734
.44005181

 ( 1)  civiceduc - hotline = 0

       F(  1,   145) =    0.00
            Prob > F =    0.9886
.98858487

 ( 1)  civiceduc - verdade = 0

       F(  1,   145) =    0.00
            Prob > F =    0.9807
.9807448

 ( 1)  hotline - verdade = 0

       F(  1,   145) =    0.00
            Prob > F =    0.9697
.96968218

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   145) =    2.31
            Prob > F =    0.0790
.07895585

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  6,   154) =   16.42
       Model |  1.22948813     6  .204914688           Prob > F      =  0.0000
    Residual |  1.92223476   154  .012482044           R-squared     =  0.3901
-------------+------------------------------           Adj R-squared =  0.3663
       Total |  3.15172289   160  .019698268           Root MSE      =  .11172

------------------------------------------------------------------------------
bsturnou~l09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0464283   .0246829     1.88   0.062    -.0023325    .0951892
     hotline |   .0517478    .024832     2.08   0.039     .0026925    .1008031
     verdade |   .0480351   .0249937     1.92   0.056    -.0013397    .0974099
         pr1 |  -.1001711    .024832    -4.03   0.000    -.1492264   -.0511158
         pr2 |   .0127654    .024832     0.51   0.608    -.0362899    .0618207
         pr3 |   .1391518   .0248398     5.60   0.000     .0900811    .1882224
       _cons |   .4252065    .022874    18.59   0.000     .3800191    .4703939
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsturnou~l09 |        41    .4378275    .1162358   .2245571   .6983862
.43782748

 ( 1)  civiceduc - hotline = 0

       F(  1,   154) =    0.05
            Prob > F =    0.8307
.83066129

 ( 1)  civiceduc - verdade = 0

       F(  1,   154) =    0.00
            Prob > F =    0.9488
.94884179

 ( 1)  hotline - verdade = 0

       F(  1,   154) =    0.02
            Prob > F =    0.8828
.88280978

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   154) =    1.95
            Prob > F =    0.1234
.12344939

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 15,   145) =    7.35
       Model |  1.36085466    15  .090723644           Prob > F      =  0.0000
    Residual |  1.79086823   145  .012350815           R-squared     =  0.4318
-------------+------------------------------           Adj R-squared =  0.3730
       Total |  3.15172289   160  .019698268           Root MSE      =  .11113

---------------------------------------------------------------------------------
bsturnoutparl09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      civiceduc |   .0516857    .024851     2.08   0.039     .0025687    .1008027
        hotline |   .0556516   .0249381     2.23   0.027     .0063624    .1049409
        verdade |   .0553319   .0253372     2.18   0.031     .0052539    .1054098
            pr1 |  -.0726351   .0272047    -2.67   0.008    -.1264041   -.0188662
            pr2 |   .0419273   .0303534     1.38   0.169     -.018065    .1019196
            pr3 |   .1344219   .0270584     4.97   0.000      .080942    .1879017
      count2009 |  -.0093632   .0038412    -2.44   0.016    -.0169553   -.0017712
      policesta |   .0205373   .0197001     1.04   0.299    -.0183992    .0594737
 policesta_miss |  -.0606034   .1175769    -0.52   0.607    -.2929894    .1717826
          sewer |   .0036182   .0302937     0.12   0.905    -.0562561    .0634924
     sewer_miss |   .2337179   .1812327     1.29   0.199    -.1244812    .5919169
     recreation |   .0298654    .024275     1.23   0.221    -.0181132     .077844
recreation_miss |    -.02194   .0544783    -0.40   0.688    -.1296141    .0857342
           road |    .020353   .0240038     0.85   0.398    -.0270896    .0677955
      road_miss |  -.0659592   .1147977    -0.57   0.566    -.2928523    .1609339
          _cons |   .4132399   .0334189    12.37   0.000     .3471888     .479291
---------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsturnou~l09 |        41    .4378275    .1162358   .2245571   .6983862
.43782748

 ( 1)  civiceduc - hotline = 0

       F(  1,   145) =    0.03
            Prob > F =    0.8746
.87456922

 ( 1)  civiceduc - verdade = 0

       F(  1,   145) =    0.02
            Prob > F =    0.8871
.88708221

 ( 1)  hotline - verdade = 0

       F(  1,   145) =    0.00
            Prob > F =    0.9901
.99009643

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   145) =    2.39
            Prob > F =    0.0708
.07083498

. 
. matrix define means=(m_bsturnoutpres09_2, m_bsturnoutpres09_3, m_bsturnoutparl09_2, m_bsturnou
> tparl09_3 \ t1_bsturnoutpres09_2, t1_bsturnoutpres09_3, t1_bsturnoutparl09_2, t1_bsturnoutparl
> 09_3 \ t2_bsturnoutpres09_2, t2_bsturnoutpres09_3, t2_bsturnoutparl09_2, t2_bsturnoutparl09_3 
> \ t3_bsturnoutpres09_2, t3_bsturnoutpres09_3, t3_bsturnoutparl09_2, t3_bsturnoutparl09_3 \ t4_
> bsturnoutpres09_2, t4_bsturnoutpres09_3, t4_bsturnoutparl09_2, t4_bsturnoutparl09_3)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_ballots.xml") replace sheet("out1
> ") 


note: results saved to outputregs_ballots.xml

. xml_tab $list2, save("outputregs_ballots.xml") append sheet("out1 stats") 


note: results saved to outputregs_ballots.xml

. estimates clear

. 
. global list1=""

. global list2=""

. 
. foreach i in $out2 {
  2. 
.         regress `i' $treat $prov if v==1 & time==1
  3.         estimates store `i'_2
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2=r(mean)
  6.         display m_`i'_2
  7.         test civiceduc = hotline
  8.         scalar define t1_`i'_2=r(p)
  9.         display t1_`i'_2
 10.         test civiceduc = verdade
 11.         scalar define t2_`i'_2=r(p)
 12.         display t2_`i'_2
 13.         test hotline = verdade
 14.         scalar define t3_`i'_2=r(p)
 15.         display t3_`i'_2
 16.         test civiceduc hotline verdade
 17.         scalar define t4_`i'_2=r(p)
 18.         display t4_`i'_2
 19.         
.         regress `i' $treat $prov $ea if v==1 & time==1
 20.         estimates store `i'_3
 21.         sum `i' if e(sample) & control == 1
 22.         scalar define m_`i'_3=r(mean)
 23.         display m_`i'_3
 24.         test civiceduc = hotline
 25.         scalar define t1_`i'_3=r(p)
 26.         display t1_`i'_3
 27.         test civiceduc = verdade
 28.         scalar define t2_`i'_3=r(p)
 29.         display t2_`i'_3
 30.         test hotline = verdade
 31.         scalar define t3_`i'_3=r(p)
 32.         display t3_`i'_3
 33.         test civiceduc hotline verdade
 34.         scalar define t4_`i'_3=r(p)
 35.         display t4_`i'_3
 36. 
.         global list1="$list1" + " `i'_2" + " `i'_3"
 37. 
. }

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  6,   154) =    4.29
       Model |  .007140668     6  .001190111           Prob > F      =  0.0005
    Residual |  .042729092   154  .000277462           R-squared     =  0.1432
-------------+------------------------------           Adj R-squared =  0.1098
       Total |   .04986976   160  .000311686           Root MSE      =  .01666

------------------------------------------------------------------------------
bsnullpres09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0041319   .0036801    -1.12   0.263    -.0114018     .003138
     hotline |  -.0047226   .0037023    -1.28   0.204    -.0120364    .0025912
     verdade |  -.0069376   .0037264    -1.86   0.065     -.014299    .0004239
         pr1 |   .0074894   .0037023     2.02   0.045     .0001756    .0148032
         pr2 |  -.0061763   .0037023    -1.67   0.097    -.0134902    .0011375
         pr3 |  -.0085099   .0037034    -2.30   0.023     -.015826   -.0011938
       _cons |   .0382051   .0034104    11.20   0.000     .0314679    .0449422
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsnullpres09 |        41    .0364498    .0262813   .0142119   .1372213
.03644975

 ( 1)  civiceduc - hotline = 0

       F(  1,   154) =    0.03
            Prob > F =    0.8734
.87344718

 ( 1)  civiceduc - verdade = 0

       F(  1,   154) =    0.57
            Prob > F =    0.4528
.45279229

 ( 1)  hotline - verdade = 0

       F(  1,   154) =    0.35
            Prob > F =    0.5555
.55550057

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   154) =    1.22
            Prob > F =    0.3058
.30577642

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 15,   145) =    3.20
       Model |  .012397254    15  .000826484           Prob > F      =  0.0001
    Residual |  .037472506   145  .000258431           R-squared     =  0.2486
-------------+------------------------------           Adj R-squared =  0.1709
       Total |   .04986976   160  .000311686           Root MSE      =  .01608

---------------------------------------------------------------------------------
   bsnullpres09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      civiceduc |  -.0033573   .0035947    -0.93   0.352    -.0104622    .0037475
        hotline |  -.0050219   .0036074    -1.39   0.166    -.0121517    .0021079
        verdade |  -.0069754   .0036651    -1.90   0.059    -.0142193    .0002685
            pr1 |   .0105533   .0039352     2.68   0.008     .0027755     .018331
            pr2 |  -.0037624   .0043907    -0.86   0.393    -.0124404    .0049156
            pr3 |  -.0081951   .0039141    -2.09   0.038    -.0159311   -.0004592
      count2009 |  -.0015682   .0005556    -2.82   0.005    -.0026664     -.00047
      policesta |  -.0056974   .0028497    -2.00   0.047    -.0113297   -.0000652
 policesta_miss |   .0037113   .0170077     0.22   0.828    -.0299038    .0373264
          sewer |  -.0016745    .004382    -0.38   0.703    -.0103354    .0069864
     sewer_miss |  -.0026652   .0262157    -0.10   0.919    -.0544794    .0491491
     recreation |   .0019216   .0035114     0.55   0.585    -.0050186    .0088618
recreation_miss |   .0081847   .0078804     1.04   0.301    -.0073906      .02376
           road |   .0048689   .0034722     1.40   0.163    -.0019938    .0117316
      road_miss |  -.0125872   .0166057    -0.76   0.450    -.0454077    .0202334
          _cons |   .0440946   .0048341     9.12   0.000     .0345402     .053649
---------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsnullpres09 |        41    .0364498    .0262813   .0142119   .1372213
.03644975

 ( 1)  civiceduc - hotline = 0

       F(  1,   145) =    0.21
            Prob > F =    0.6470
.64703953

 ( 1)  civiceduc - verdade = 0

       F(  1,   145) =    0.95
            Prob > F =    0.3308
.33079532

 ( 1)  hotline - verdade = 0

       F(  1,   145) =    0.28
            Prob > F =    0.6003
.60030978

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   145) =    1.32
            Prob > F =    0.2706
.27061644

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  6,   154) =    4.89
       Model |  .006520564     6  .001086761           Prob > F      =  0.0001
    Residual |  .034215964   154  .000222182           R-squared     =  0.1601
-------------+------------------------------           Adj R-squared =  0.1273
       Total |  .040736528   160  .000254603           Root MSE      =  .01491

------------------------------------------------------------------------------
bsnullparl09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0060983   .0032931    -1.85   0.066    -.0126038    .0004072
     hotline |  -.0033206    .003313    -1.00   0.318    -.0098654    .0032242
     verdade |  -.0031648   .0033346    -0.95   0.344    -.0097523    .0034226
         pr1 |   .0012587    .003313     0.38   0.705    -.0052862    .0078035
         pr2 |  -.0084191    .003313    -2.54   0.012    -.0149639   -.0018742
         pr3 |  -.0130901    .003314    -3.95   0.000    -.0196369   -.0065432
       _cons |    .033317   .0030518    10.92   0.000     .0272883    .0393458
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsnullparl09 |        41    .0283779    .0209151   .0079787   .1172529
.02837789

 ( 1)  civiceduc - hotline = 0

       F(  1,   154) =    0.70
            Prob > F =    0.4031
.40309407

 ( 1)  civiceduc - verdade = 0

       F(  1,   154) =    0.77
            Prob > F =    0.3805
.38053754

 ( 1)  hotline - verdade = 0

       F(  1,   154) =    0.00
            Prob > F =    0.9630
.96302784

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   154) =    1.15
            Prob > F =    0.3324
.33242957

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 15,   145) =    2.81
       Model |  .009186847    15  .000612456           Prob > F      =  0.0007
    Residual |  .031549681   145  .000217584           R-squared     =  0.2255
-------------+------------------------------           Adj R-squared =  0.1454
       Total |  .040736528   160  .000254603           Root MSE      =  .01475

---------------------------------------------------------------------------------
   bsnullparl09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      civiceduc |  -.0060274   .0032984    -1.83   0.070    -.0125466    .0004918
        hotline |  -.0035375     .00331    -1.07   0.287    -.0100796    .0030046
        verdade |  -.0025378    .003363    -0.75   0.452    -.0091846     .004109
            pr1 |   .0038516   .0036108     1.07   0.288    -.0032851    .0109883
            pr2 |  -.0060098   .0040288    -1.49   0.138    -.0139726    .0019529
            pr3 |  -.0146724   .0035914    -4.09   0.000    -.0217708   -.0075741
      count2009 |  -.0012163   .0005098    -2.39   0.018     -.002224   -.0002087
      policesta |  -.0023886   .0026148    -0.91   0.362    -.0075566    .0027794
 policesta_miss |   .0004031   .0156059     0.03   0.979    -.0304413    .0312474
          sewer |  -.0002364   .0040208    -0.06   0.953    -.0081834    .0077107
     sewer_miss |  -.0128804   .0240548    -0.54   0.593    -.0604237     .034663
     recreation |   .0042008    .003222     1.30   0.194    -.0021673     .010569
recreation_miss |   .0076386   .0072308     1.06   0.293    -.0066529    .0219301
           road |  -.0003954    .003186    -0.12   0.901    -.0066924    .0059016
      road_miss |  -.0014533    .015237    -0.10   0.924    -.0315686     .028662
          _cons |   .0360681   .0044357     8.13   0.000     .0273012     .044835
---------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsnullparl09 |        41    .0283779    .0209151   .0079787   .1172529
.02837789

 ( 1)  civiceduc - hotline = 0

       F(  1,   145) =    0.56
            Prob > F =    0.4557
.45567483

 ( 1)  civiceduc - verdade = 0

       F(  1,   145) =    1.05
            Prob > F =    0.3067
.30674851

 ( 1)  hotline - verdade = 0

       F(  1,   145) =    0.09
            Prob > F =    0.7700
.77004597

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   145) =    1.14
            Prob > F =    0.3351
.335053

. 
. matrix define means=(m_bsnullpres09_2, m_bsnullpres09_3, m_bsnullparl09_2, m_bsnullparl09_3 \ 
> t1_bsnullpres09_2, t1_bsnullpres09_3, t1_bsnullparl09_2, t1_bsnullparl09_3 \ t2_bsnullpres09_2
> , t2_bsnullpres09_3, t2_bsnullparl09_2, t2_bsnullparl09_3 \ t3_bsnullpres09_2, t3_bsnullpres09
> _3, t3_bsnullparl09_2, t3_bsnullparl09_3 \ t4_bsnullpres09_2, t4_bsnullpres09_3, t4_bsnullparl
> 09_2, t4_bsnullparl09_3)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_ballots.xml") append sheet("out2"
> ) 


note: results saved to outputregs_ballots.xml

. xml_tab $list2, save("outputregs_ballots.xml") append sheet("out2 stats") 


note: results saved to outputregs_ballots.xml

. estimates clear

. 
. global list1=""

. global list2=""

. 
. foreach i in $out3 {
  2. 
.         regress `i' $treat $prov if v==1 & time==1
  3.         estimates store `i'_2
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2=r(mean)
  6.         display m_`i'_2
  7.         test civiceduc = hotline
  8.         scalar define t1_`i'_2=r(p)
  9.         display t1_`i'_2
 10.         test civiceduc = verdade
 11.         scalar define t2_`i'_2=r(p)
 12.         display t2_`i'_2
 13.         test hotline = verdade
 14.         scalar define t3_`i'_2=r(p)
 15.         display t3_`i'_2
 16.         test civiceduc hotline verdade
 17.         scalar define t4_`i'_2=r(p)
 18.         display t4_`i'_2
 19.         
.         regress `i' $treat $prov $ea if v==1 & time==1
 20.         estimates store `i'_3
 21.         sum `i' if e(sample) & control == 1
 22.         scalar define m_`i'_3=r(mean)
 23.         display m_`i'_3
 24.         test civiceduc = hotline
 25.         scalar define t1_`i'_3=r(p)
 26.         display t1_`i'_3
 27.         test civiceduc = verdade
 28.         scalar define t2_`i'_3=r(p)
 29.         display t2_`i'_3
 30.         test hotline = verdade
 31.         scalar define t3_`i'_3=r(p)
 32.         display t3_`i'_3
 33.         test civiceduc hotline verdade
 34.         scalar define t4_`i'_3=r(p)
 35.         display t4_`i'_3
 36.         
.         global list1="$list1" + " `i'_2" + " `i'_3"
 37. 
. }

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  6,   154) =   12.21
       Model |  .140126399     6    .0233544           Prob > F      =  0.0000
    Residual |  .294669642   154  .001913439           R-squared     =  0.3223
-------------+------------------------------           Adj R-squared =  0.2959
       Total |  .434796041   160  .002717475           Root MSE      =  .04374

------------------------------------------------------------------------------
bsblankpr~09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0013723   .0096641    -0.14   0.887    -.0204636     .017719
     hotline |  -.0010939   .0097225    -0.11   0.911    -.0203005    .0181127
     verdade |  -.0049536   .0097858    -0.51   0.613    -.0242853    .0143781
         pr1 |  -.0003696   .0097225    -0.04   0.970    -.0195762     .018837
         pr2 |  -.0604331   .0097225    -6.22   0.000    -.0796397   -.0412265
         pr3 |  -.0576843   .0097255    -5.93   0.000    -.0768969   -.0384717
       _cons |   .0863549   .0089559     9.64   0.000     .0686627    .1040471
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsblankpr~09 |        41    .0574556    .0541197          0   .2265813
.0574556

 ( 1)  civiceduc - hotline = 0

       F(  1,   154) =    0.00
            Prob > F =    0.9772
.97719427

 ( 1)  civiceduc - verdade = 0

       F(  1,   154) =    0.13
            Prob > F =    0.7150
.71497686

 ( 1)  hotline - verdade = 0

       F(  1,   154) =    0.15
            Prob > F =    0.6956
.69556558

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   154) =    0.10
            Prob > F =    0.9625
.96245872

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 15,   145) =    9.32
       Model |  .213438112    15  .014229207           Prob > F      =  0.0000
    Residual |  .221357929   145  .001526606           R-squared     =  0.4909
-------------+------------------------------           Adj R-squared =  0.4382
       Total |  .434796041   160  .002717475           Root MSE      =  .03907

---------------------------------------------------------------------------------
  bsblankpres09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      civiceduc |   .0009789   .0087369     0.11   0.911    -.0162893    .0182471
        hotline |  -.0007505   .0087676    -0.09   0.932    -.0180793    .0165783
        verdade |  -.0038173   .0089079    -0.43   0.669    -.0214234    .0137887
            pr1 |   .0105848   .0095644     1.11   0.270     -.008319    .0294885
            pr2 |  -.0484091   .0106714    -4.54   0.000    -.0695008   -.0273175
            pr3 |  -.0637148    .009513    -6.70   0.000    -.0825168   -.0449127
      count2009 |  -.0069835   .0013505    -5.17   0.000    -.0096526   -.0043143
      policesta |  -.0151096    .006926    -2.18   0.031    -.0287987   -.0014206
 policesta_miss |   .0067063   .0413369     0.16   0.871    -.0749944     .088407
          sewer |  -.0012897   .0106504    -0.12   0.904    -.0223399    .0197605
     sewer_miss |   .0470508   .0637166     0.74   0.461    -.0788823     .172984
     recreation |   .0086071   .0085345     1.01   0.315    -.0082609    .0254751
recreation_miss |  -.0014304   .0191531    -0.07   0.941    -.0392857     .036425
           road |   .0021279   .0084391     0.25   0.801    -.0145517    .0188074
      road_miss |  -.0592694   .0403598    -1.47   0.144    -.1390389    .0205001
          _cons |   .1140676   .0117492     9.71   0.000     .0908458    .1372894
---------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsblankpr~09 |        41    .0574556    .0541197          0   .2265813
.0574556

 ( 1)  civiceduc - hotline = 0

       F(  1,   145) =    0.04
            Prob > F =    0.8448
.84477814

 ( 1)  civiceduc - verdade = 0

       F(  1,   145) =    0.28
            Prob > F =    0.5954
.59539093

 ( 1)  hotline - verdade = 0

       F(  1,   145) =    0.12
            Prob > F =    0.7350
.73496135

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   145) =    0.10
            Prob > F =    0.9572
.95720172

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  6,   154) =    8.09
       Model |  .154541576     6  .025756929           Prob > F      =  0.0000
    Residual |  .490493047   154   .00318502           R-squared     =  0.2396
-------------+------------------------------           Adj R-squared =  0.2100
       Total |  .645034623   160  .004031466           Root MSE      =  .05644

------------------------------------------------------------------------------
bsblankpa~09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0032405   .0124684     0.26   0.795    -.0213906    .0278716
     hotline |   .0020267   .0125437     0.16   0.872    -.0227532    .0268066
     verdade |  -.0083185   .0126254    -0.66   0.511    -.0332598    .0166228
         pr1 |  -.0162924   .0125437    -1.30   0.196    -.0410722    .0084875
         pr2 |   -.054179   .0125437    -4.32   0.000    -.0789589   -.0293991
         pr3 |  -.0779676   .0125476    -6.21   0.000    -.1027553     -.05318
       _cons |   .1170921   .0115546    10.13   0.000     .0942661    .1399181
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsblankpa~09 |        41    .0808875    .0600231          0   .2608696
.08088748

 ( 1)  civiceduc - hotline = 0

       F(  1,   154) =    0.01
            Prob > F =    0.9230
.92303781

 ( 1)  civiceduc - verdade = 0

       F(  1,   154) =    0.84
            Prob > F =    0.3615
.36149012

 ( 1)  hotline - verdade = 0

       F(  1,   154) =    0.66
            Prob > F =    0.4166
.4166289

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   154) =    0.34
            Prob > F =    0.7990
.79896051

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 15,   145) =    6.87
       Model |  .267938372    15  .017862558           Prob > F      =  0.0000
    Residual |  .377096251   145  .002600664           R-squared     =  0.4154
-------------+------------------------------           Adj R-squared =  0.3549
       Total |  .645034623   160  .004031466           Root MSE      =    .051

---------------------------------------------------------------------------------
  bsblankparl09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      civiceduc |   .0050901   .0114035     0.45   0.656    -.0174484    .0276287
        hotline |    .001255   .0114435     0.11   0.913    -.0213625    .0238726
        verdade |  -.0080909   .0116266    -0.70   0.488    -.0310704    .0148886
            pr1 |  -.0086912   .0124835    -0.70   0.487    -.0333645     .015982
            pr2 |  -.0361787   .0139284    -2.60   0.010    -.0637076   -.0086497
            pr3 |  -.0857992   .0124164    -6.91   0.000    -.1103398   -.0612587
      count2009 |  -.0067331   .0017626    -3.82   0.000    -.0102169   -.0032493
      policesta |  -.0228231   .0090399    -2.52   0.013    -.0406901   -.0049562
 policesta_miss |  -.0217519   .0539531    -0.40   0.687     -.128388    .0848842
          sewer |  -.0178199    .013901    -1.28   0.202    -.0452947    .0096549
     sewer_miss |   .0863265   .0831631     1.04   0.301    -.0780421     .250695
     recreation |   .0083571   .0111392     0.75   0.454    -.0136591    .0303733
recreation_miss |  -.0053747   .0249987    -0.21   0.830    -.0547837    .0440343
           road |   .0007061   .0110147     0.06   0.949    -.0210641    .0224763
      road_miss |  -.0848673   .0526778    -1.61   0.109    -.1889828    .0192482
          _cons |   .1522079   .0153351     9.93   0.000     .1218987    .1825171
---------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsblankpa~09 |        41    .0808875    .0600231          0   .2608696
.08088748

 ( 1)  civiceduc - hotline = 0

       F(  1,   145) =    0.11
            Prob > F =    0.7394
.73942835

 ( 1)  civiceduc - verdade = 0

       F(  1,   145) =    1.26
            Prob > F =    0.2643
.26429648

 ( 1)  hotline - verdade = 0

       F(  1,   145) =    0.63
            Prob > F =    0.4297
.42968774

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   145) =    0.44
            Prob > F =    0.7256
.72558756

. 
. matrix define means=(m_bsblankpres09_2, m_bsblankpres09_3, m_bsblankparl09_2, m_bsblankparl09_
> 3 \ t1_bsblankpres09_2, t1_bsblankpres09_3, t1_bsblankparl09_2, t1_bsblankparl09_3 \ t2_bsblan
> kpres09_2, t2_bsblankpres09_3, t2_bsblankparl09_2, t2_bsblankparl09_3 \ t3_bsblankpres09_2, t3
> _bsblankpres09_3, t3_bsblankparl09_2, t3_bsblankparl09_3 \ t4_bsblankpres09_2, t4_bsblankpres0
> 9_3, t4_bsblankparl09_2, t4_bsblankparl09_3)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_ballots.xml") append sheet("out3"
> ) 


note: results saved to outputregs_ballots.xml

. xml_tab $list2, save("outputregs_ballots.xml") append sheet("out3 stats") 


note: results saved to outputregs_ballots.xml

. estimates clear

. 
. global list1=""

. global list2=""

. 
. foreach i in $out4 {
  2. 
.         regress `i' $treat $prov if v==1 & time==1
  3.         estimates store `i'_2
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2=r(mean)
  6.         display m_`i'_2
  7.         test civiceduc = hotline
  8.         scalar define t1_`i'_2=r(p)
  9.         display t1_`i'_2
 10.         test civiceduc = verdade
 11.         scalar define t2_`i'_2=r(p)
 12.         display t2_`i'_2
 13.         test hotline = verdade
 14.         scalar define t3_`i'_2=r(p)
 15.         display t3_`i'_2
 16.         test civiceduc hotline verdade
 17.         scalar define t4_`i'_2=r(p)
 18.         display t4_`i'_2
 19.         
.         regress `i' $treat $prov $ea if v==1 & time==1
 20.         estimates store `i'_3
 21.         sum `i' if e(sample) & control == 1
 22.         scalar define m_`i'_3=r(mean)
 23.         display m_`i'_3
 24.         test civiceduc = hotline
 25.         scalar define t1_`i'_3=r(p)
 26.         display t1_`i'_3
 27.         test civiceduc = verdade
 28.         scalar define t2_`i'_3=r(p)
 29.         display t2_`i'_3
 30.         test hotline = verdade
 31.         scalar define t3_`i'_3=r(p)
 32.         display t3_`i'_3
 33.         test civiceduc hotline verdade
 34.         scalar define t4_`i'_3=r(p)
 35.         display t4_`i'_3
 36.         
.         global list1="$list1" + " `i'_2" + " `i'_3"
 37. 
. }

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  6,   154) =   53.73
       Model |  2.65173403     6  .441955672           Prob > F      =  0.0000
    Residual |  1.26675084   154  .008225655           R-squared     =  0.6767
-------------+------------------------------           Adj R-squared =  0.6641
       Total |  3.91848487   160   .02449053           Root MSE      =   .0907

------------------------------------------------------------------------------
  bsguebas09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0486965   .0200373     2.43   0.016      .009113    .0882799
     hotline |   .0219191   .0201583     1.09   0.279    -.0179034    .0617416
     verdade |   .0401293   .0202896     1.98   0.050     .0000475    .0802112
         pr1 |   -.119165   .0201583    -5.91   0.000    -.1589875   -.0793425
         pr2 |   .1274182   .0201583     6.32   0.000     .0875957    .1672407
         pr3 |   .2163976   .0201646    10.73   0.000     .1765626    .2562325
       _cons |   .6680723   .0185689    35.98   0.000     .6313897    .7047549
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  bsguebas09 |        41    .7228651     .184084   .1590538   .9491262
.72286514

 ( 1)  civiceduc - hotline = 0

       F(  1,   154) =    1.76
            Prob > F =    0.1860
.18602726

 ( 1)  civiceduc - verdade = 0

       F(  1,   154) =    0.18
            Prob > F =    0.6735
.67353386

 ( 1)  hotline - verdade = 0

       F(  1,   154) =    0.80
            Prob > F =    0.3737
.37371684

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   154) =    2.30
            Prob > F =    0.0791
.07906239

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 15,   145) =   22.65
       Model |  2.74630112    15  .183086742           Prob > F      =  0.0000
    Residual |  1.17218375   145  .008084026           R-squared     =  0.7009
-------------+------------------------------           Adj R-squared =  0.6699
       Total |  3.91848487   160   .02449053           Root MSE      =  .08991

---------------------------------------------------------------------------------
     bsguebas09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      civiceduc |    .046221   .0201053     2.30   0.023     .0064838    .0859582
        hotline |   .0219375   .0201758     1.09   0.279    -.0179391    .0618141
        verdade |   .0411382   .0204986     2.01   0.047     .0006235    .0816528
            pr1 |  -.1209315   .0220095    -5.49   0.000    -.1644323   -.0774306
            pr2 |   .1420291   .0245569     5.78   0.000     .0934934    .1905649
            pr3 |   .2169997   .0218911     9.91   0.000     .1737328    .2602666
      count2009 |   .0041585   .0031077     1.34   0.183    -.0019837    .0103007
      policesta |    .030029    .015938     1.88   0.062    -.0014719    .0615298
 policesta_miss |    -.05335   .0951235    -0.56   0.576    -.2413578    .1346579
          sewer |  -.0330703   .0245086    -1.35   0.179    -.0815105    .0153699
     sewer_miss |  -.0174359   .1466232    -0.12   0.906    -.3072307    .2723588
     recreation |   .0104269   .0196393     0.53   0.596    -.0283894    .0492431
recreation_miss |  -.0177233   .0440747    -0.40   0.688    -.1048352    .0693887
           road |     .01332   .0194199     0.69   0.494    -.0250626    .0517026
      road_miss |   .0785239   .0928751     0.85   0.399      -.10504    .2620878
          _cons |   .6279887    .027037    23.23   0.000     .5745512    .6814261
---------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  bsguebas09 |        41    .7228651     .184084   .1590538   .9491262
.72286514

 ( 1)  civiceduc - hotline = 0

       F(  1,   145) =    1.43
            Prob > F =    0.2333
.23332699

 ( 1)  civiceduc - verdade = 0

       F(  1,   145) =    0.06
            Prob > F =    0.8067
.80672381

 ( 1)  hotline - verdade = 0

       F(  1,   145) =    0.85
            Prob > F =    0.3576
.35763684

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   145) =    2.17
            Prob > F =    0.0941
.09409736

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  6,   154) =   35.65
       Model |  1.11578377     6  .185963962           Prob > F      =  0.0000
    Residual |  .803428626   154  .005217069           R-squared     =  0.5814
-------------+------------------------------           Adj R-squared =  0.5651
       Total |   1.9192124   160  .011995078           Root MSE      =  .07223

------------------------------------------------------------------------------
bsdhlakama09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0303871   .0159576    -1.90   0.059    -.0619111    .0011369
     hotline |  -.0104254    .016054    -0.65   0.517    -.0421398     .021289
     verdade |  -.0150904   .0161585    -0.93   0.352    -.0470114    .0168306
         pr1 |   .0876114    .016054     5.46   0.000      .055897    .1193257
         pr2 |  -.0946673    .016054    -5.90   0.000    -.1263817    -.062953
         pr3 |  -.1216099    .016059    -7.57   0.000    -.1533343   -.0898856
       _cons |   .1453377   .0147881     9.83   0.000     .1161239    .1745515
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsdhlakama09 |        41    .1139558    .1327448   .0017921   .5856444
.11395576

 ( 1)  civiceduc - hotline = 0

       F(  1,   154) =    1.55
            Prob > F =    0.2156
.21560601

 ( 1)  civiceduc - verdade = 0

       F(  1,   154) =    0.90
            Prob > F =    0.3454
.34544273

 ( 1)  hotline - verdade = 0

       F(  1,   154) =    0.08
            Prob > F =    0.7745
.77452279

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   154) =    1.25
            Prob > F =    0.2930
.29303545

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 15,   145) =   15.06
       Model |  1.16903131    15  .077935421           Prob > F      =  0.0000
    Residual |  .750181087   145  .005173663           R-squared     =  0.6091
-------------+------------------------------           Adj R-squared =  0.5687
       Total |   1.9192124   160  .011995078           Root MSE      =  .07193

---------------------------------------------------------------------------------
   bsdhlakama09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      civiceduc |  -.0316101    .016084    -1.97   0.051    -.0633995    .0001794
        hotline |  -.0122868   .0161404    -0.76   0.448    -.0441878    .0196141
        verdade |  -.0164386   .0163987    -1.00   0.318    -.0488499    .0159728
            pr1 |   .0808567   .0176074     4.59   0.000     .0460564     .115657
            pr2 |  -.1027182   .0196453    -5.23   0.000    -.1415463   -.0638901
            pr3 |  -.1200051   .0175127    -6.85   0.000    -.1546183   -.0853919
      count2009 |   .0003146   .0024861     0.13   0.899    -.0045991    .0052283
      policesta |   -.004949   .0127503    -0.39   0.698    -.0301494    .0202514
 policesta_miss |   .0116331    .076098     0.15   0.879    -.1387715    .1620377
          sewer |   .0055238   .0196066     0.28   0.779     -.033228    .0442755
     sewer_miss |   .0413689   .1172972     0.35   0.725    -.1904643    .2732022
     recreation |   -.024291   .0157113    -1.55   0.124    -.0553436    .0067617
recreation_miss |   -.000621   .0352594    -0.02   0.986    -.0703098    .0690677
           road |  -.0205097   .0155357    -1.32   0.189    -.0512154     .010196
      road_miss |  -.0053149   .0742993    -0.07   0.943    -.1521643    .1415346
          _cons |   .1706499   .0216293     7.89   0.000     .1279004    .2133994
---------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsdhlakama09 |        41    .1139558    .1327448   .0017921   .5856444
.11395576

 ( 1)  civiceduc - hotline = 0

       F(  1,   145) =    1.42
            Prob > F =    0.2358
.23581126

 ( 1)  civiceduc - verdade = 0

       F(  1,   145) =    0.84
            Prob > F =    0.3620
.36197091

 ( 1)  hotline - verdade = 0

       F(  1,   145) =    0.06
            Prob > F =    0.8034
.80338455

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   145) =    1.31
            Prob > F =    0.2721
.27211179

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  6,   154) =   11.41
       Model |  .098349893     6  .016391649           Prob > F      =  0.0000
    Residual |  .221236864   154  .001436603           R-squared     =  0.3077
-------------+------------------------------           Adj R-squared =  0.2808
       Total |  .319586757   160  .001997417           Root MSE      =   .0379

------------------------------------------------------------------------------
 bssimango09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0128051   .0083738    -1.53   0.128    -.0293474    .0037372
     hotline |  -.0056771   .0084244    -0.67   0.501    -.0223194    .0109651
     verdade |  -.0131478   .0084792    -1.55   0.123    -.0298984    .0036029
         pr1 |   .0244339   .0084244     2.90   0.004     .0077916    .0410761
         pr2 |   .0338586   .0084244     4.02   0.000     .0172164    .0505009
         pr3 |  -.0285934    .008427    -3.39   0.001    -.0452408    -.011946
       _cons |   .0620301   .0077601     7.99   0.000        .0467    .0773601
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 bssimango09 |        41    .0692737    .0559971   .0069903   .2279817
.06927374

 ( 1)  civiceduc - hotline = 0

       F(  1,   154) =    0.72
            Prob > F =    0.3988
.39880349

 ( 1)  civiceduc - verdade = 0

       F(  1,   154) =    0.00
            Prob > F =    0.9678
.96782687

 ( 1)  hotline - verdade = 0

       F(  1,   154) =    0.77
            Prob > F =    0.3825
.38252491

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   154) =    1.12
            Prob > F =    0.3439
.3439327

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 15,   145) =    8.41
       Model |  .148699231    15  .009913282           Prob > F      =  0.0000
    Residual |  .170887526   145  .001178535           R-squared     =  0.4653
-------------+------------------------------           Adj R-squared =  0.4100
       Total |  .319586757   160  .001997417           Root MSE      =  .03433

---------------------------------------------------------------------------------
    bssimango09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      civiceduc |  -.0122325   .0076766    -1.59   0.113    -.0274049      .00294
        hotline |  -.0038783   .0077035    -0.50   0.615    -.0191039    .0113474
        verdade |  -.0139069   .0078268    -1.78   0.078    -.0293762    .0015624
            pr1 |   .0189367   .0084036     2.25   0.026     .0023273    .0355462
            pr2 |   .0128606   .0093763     1.37   0.172    -.0056712    .0313925
            pr3 |  -.0250847   .0083584    -3.00   0.003    -.0416048   -.0085646
      count2009 |   .0040786   .0011866     3.44   0.001     .0017334    .0064238
      policesta |  -.0042729   .0060854    -0.70   0.484    -.0163005    .0077547
 policesta_miss |   .0312993     .03632     0.86   0.390    -.0404857    .1030842
          sewer |   .0305107   .0093578     3.26   0.001     .0120153    .0490061
     sewer_miss |  -.0683187   .0559835    -1.22   0.224    -.1789678    .0423304
     recreation |   .0033354   .0074987     0.44   0.657    -.0114854    .0181562
recreation_miss |   .0115899   .0168286     0.69   0.492     -.021671    .0448509
           road |    .000193   .0074149     0.03   0.979    -.0144622    .0148482
      road_miss |  -.0013524   .0354615    -0.04   0.970    -.0714406    .0687357
          _cons |   .0431993   .0103232     4.18   0.000     .0227958    .0636027
---------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 bssimango09 |        41    .0692737    .0559971   .0069903   .2279817
.06927374

 ( 1)  civiceduc - hotline = 0

       F(  1,   145) =    1.16
            Prob > F =    0.2827
.28265997

 ( 1)  civiceduc - verdade = 0

       F(  1,   145) =    0.04
            Prob > F =    0.8328
.83281351

 ( 1)  hotline - verdade = 0

       F(  1,   145) =    1.59
            Prob > F =    0.2088
.20884586

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   145) =    1.47
            Prob > F =    0.2262
.22619616

. 
. matrix define means=(m_bsguebas09_2, m_bsguebas09_3, m_bsdhlakama09_2, m_bsdhlakama09_3, m_bss
> imango09_2, m_bssimango09_3 \ t1_bsguebas09_2, t1_bsguebas09_3, t1_bsdhlakama09_2, t1_bsdhlaka
> ma09_3, t1_bssimango09_2, t1_bssimango09_3 \ t2_bsguebas09_2, t2_bsguebas09_3, t2_bsdhlakama09
> _2, t2_bsdhlakama09_3, t2_bssimango09_2, t2_bssimango09_3 \ t3_bsguebas09_2, t3_bsguebas09_3, 
> t3_bsdhlakama09_2, t3_bsdhlakama09_3, t3_bssimango09_2, t3_bssimango09_3 \ t4_bsguebas09_2, t4
> _bsguebas09_3, t4_bsdhlakama09_2, t4_bsdhlakama09_3, t4_bssimango09_2, t4_bssimango09_3)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_ballots.xml") append sheet("out4"
> ) 


note: results saved to outputregs_ballots.xml

. xml_tab $list2, save("outputregs_ballots.xml") append sheet("out4 stats") 


note: results saved to outputregs_ballots.xml

. estimates clear

. 
. global list1=""

. global list2=""

. 
. foreach i in $out5 {
  2. 
.         regress `i' $treat $prov if v==1 & time==1
  3.         estimates store `i'_2
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2=r(mean)
  6.         display m_`i'_2
  7.         test civiceduc = hotline
  8.         scalar define t1_`i'_2=r(p)
  9.         display t1_`i'_2
 10.         test civiceduc = verdade
 11.         scalar define t2_`i'_2=r(p)
 12.         display t2_`i'_2
 13.         test hotline = verdade
 14.         scalar define t3_`i'_2=r(p)
 15.         display t3_`i'_2
 16.         test civiceduc hotline verdade
 17.         scalar define t4_`i'_2=r(p)
 18.         display t4_`i'_2
 19. 
.         regress `i' $treat $prov $ea if v==1 & time==1
 20.         estimates store `i'_3
 21.         sum `i' if e(sample) & control == 1
 22.         scalar define m_`i'_3=r(mean)
 23.         display m_`i'_3
 24.         test civiceduc = hotline
 25.         scalar define t1_`i'_3=r(p)
 26.         display t1_`i'_3
 27.         test civiceduc = verdade
 28.         scalar define t2_`i'_3=r(p)
 29.         display t2_`i'_3
 30.         test hotline = verdade
 31.         scalar define t3_`i'_3=r(p)
 32.         display t3_`i'_3
 33.         test civiceduc hotline verdade
 34.         scalar define t4_`i'_3=r(p)
 35.         display t4_`i'_3
 36.         
.         global list1="$list1" + " `i'_2" + " `i'_3"
 37. 
. }

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  6,   154) =   48.20
       Model |  2.67768955     6  .446281592           Prob > F      =  0.0000
    Residual |  1.42580367   154  .009258465           R-squared     =  0.6525
-------------+------------------------------           Adj R-squared =  0.6390
       Total |  4.10349322   160  .025646833           Root MSE      =  .09622

------------------------------------------------------------------------------
 bsfrelimo09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0404137   .0212581     1.90   0.059    -.0015814    .0824087
     hotline |   .0182075   .0213864     0.85   0.396    -.0240411    .0604562
     verdade |   .0339956   .0215257     1.58   0.116    -.0085282    .0765194
         pr1 |  -.0934805   .0213864    -4.37   0.000    -.1357291   -.0512318
         pr2 |   .1400408   .0213864     6.55   0.000     .0977922    .1822894
         pr3 |   .2407778   .0213931    11.25   0.000     .1985159    .2830396
       _cons |   .6517666   .0197002    33.08   0.000     .6128492    .6906841
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 bsfrelimo09 |        41    .7218491    .1881129   .1647635   .9476358
.7218491

 ( 1)  civiceduc - hotline = 0

       F(  1,   154) =    1.08
            Prob > F =    0.3007
.30074581

 ( 1)  civiceduc - verdade = 0

       F(  1,   154) =    0.09
            Prob > F =    0.7661
.76605547

 ( 1)  hotline - verdade = 0

       F(  1,   154) =    0.53
            Prob > F =    0.4671
.46708055

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   154) =    1.43
            Prob > F =    0.2359
.23594593

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 15,   145) =   22.15
       Model |   2.8566812    15  .190445413           Prob > F      =  0.0000
    Residual |  1.24681202   145  .008598704           R-squared     =  0.6962
-------------+------------------------------           Adj R-squared =  0.6647
       Total |  4.10349322   160  .025646833           Root MSE      =  .09273

---------------------------------------------------------------------------------
    bsfrelimo09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      civiceduc |    .038983   .0207354     1.88   0.062    -.0019997    .0799657
        hotline |   .0201509   .0208081     0.97   0.334    -.0209755    .0612773
        verdade |   .0359677   .0211411     1.70   0.091    -.0058168    .0777522
            pr1 |  -.0944042   .0226993    -4.16   0.000    -.1392684     -.04954
            pr2 |   .1389217   .0253266     5.49   0.000     .0888648    .1889786
            pr3 |   .2447915   .0225772    10.84   0.000     .2001685    .2894145
      count2009 |    .006077   .0032051     1.90   0.060    -.0002578    .0124117
      policesta |   .0401118   .0164375     2.44   0.016     .0076236    .0725999
 policesta_miss |  -.0121955   .0981049    -0.12   0.901    -.2060959    .1817049
          sewer |  -.0026515   .0252767    -0.10   0.917      -.05261    .0473069
     sewer_miss |  -.0712364   .1512186    -0.47   0.638    -.3701138    .2276411
     recreation |   .0125423   .0202548     0.62   0.537    -.0274905    .0525752
recreation_miss |  -.0068037   .0454561    -0.15   0.881    -.0966458    .0830385
           road |   .0150906   .0200285     0.75   0.452    -.0244949    .0546762
      road_miss |   .0947335    .095786     0.99   0.324    -.0945836    .2840506
          _cons |    .591622   .0278844    21.22   0.000     .5365097    .6467343
---------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 bsfrelimo09 |        41    .7218491    .1881129   .1647635   .9476358
.7218491

 ( 1)  civiceduc - hotline = 0

       F(  1,   145) =    0.81
            Prob > F =    0.3696
.36964234

 ( 1)  civiceduc - verdade = 0

       F(  1,   145) =    0.02
            Prob > F =    0.8881
.88807824

 ( 1)  hotline - verdade = 0

       F(  1,   145) =    0.54
            Prob > F =    0.4623
.46226651

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   145) =    1.48
            Prob > F =    0.2232
.22320286

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  6,   154) =   44.83
       Model |  1.34398922     6  .223998203           Prob > F      =  0.0000
    Residual |  .769465405   154  .004996529           R-squared     =  0.6359
-------------+------------------------------           Adj R-squared =  0.6217
       Total |  2.11345462   160  .013209091           Root MSE      =  .07069

------------------------------------------------------------------------------
  bsrenamo09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0362002   .0156167    -2.32   0.022    -.0670507   -.0053497
     hotline |  -.0139104    .015711    -0.89   0.377    -.0449473    .0171264
     verdade |  -.0190801   .0158133    -1.21   0.229    -.0503191    .0121589
         pr1 |   .1141238    .015711     7.26   0.000      .083087    .1451607
         pr2 |  -.0739298    .015711    -4.71   0.000    -.1049666    -.042893
         pr3 |   -.127267   .0157159    -8.10   0.000    -.1583135   -.0962204
       _cons |   .1575129   .0144722    10.88   0.000     .1289232    .1861025
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  bsrenamo09 |        41    .1362756    .1364646          0   .5978793
.13627558

 ( 1)  civiceduc - hotline = 0

       F(  1,   154) =    2.01
            Prob > F =    0.1580
.15799672

 ( 1)  civiceduc - verdade = 0

       F(  1,   154) =    1.17
            Prob > F =    0.2808
.28081131

 ( 1)  hotline - verdade = 0

       F(  1,   154) =    0.11
            Prob > F =    0.7457
.7456551

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   154) =    1.83
            Prob > F =    0.1434
.14335627

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 15,   145) =   18.90
       Model |  1.39831473    15  .093220982           Prob > F      =  0.0000
    Residual |  .715139897   145  .004931999           R-squared     =  0.6616
-------------+------------------------------           Adj R-squared =  0.6266
       Total |  2.11345462   160  .013209091           Root MSE      =  .07023

---------------------------------------------------------------------------------
     bsrenamo09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      civiceduc |  -.0375071   .0157039    -2.39   0.018    -.0685452    -.006469
        hotline |  -.0148611    .015759    -0.94   0.347    -.0460081    .0162858
        verdade |  -.0212799   .0160111    -1.33   0.186    -.0529253    .0103655
            pr1 |   .1035985   .0171912     6.03   0.000     .0696207    .1375763
            pr2 |  -.0930093    .019181    -4.85   0.000    -.1309198   -.0550989
            pr3 |  -.1226369   .0170988    -7.17   0.000     -.156432   -.0888418
      count2009 |    .002817   .0024274     1.16   0.248    -.0019806    .0076146
      policesta |  -.0071591   .0124489    -0.58   0.566    -.0317639    .0174457
 policesta_miss |   .0251522   .0742995     0.34   0.735    -.1216977    .1720021
          sewer |   .0196821   .0191433     1.03   0.306    -.0181538     .057518
     sewer_miss |  -.0015148    .114525    -0.01   0.989    -.2278688    .2248391
     recreation |  -.0254605   .0153399    -1.66   0.099    -.0557792    .0048583
recreation_miss |   .0010394   .0344261     0.03   0.976    -.0670024    .0690811
           road |  -.0194254   .0151685    -1.28   0.202    -.0494054    .0105546
      road_miss |   .0091983   .0725432     0.13   0.899    -.1341805    .1525771
          _cons |   .1736418   .0211181     8.22   0.000     .1319026    .2153809
---------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  bsrenamo09 |        41    .1362756    .1364646          0   .5978793
.13627558

 ( 1)  civiceduc - hotline = 0

       F(  1,   145) =    2.04
            Prob > F =    0.1552
.15517238

 ( 1)  civiceduc - verdade = 0

       F(  1,   145) =    1.00
            Prob > F =    0.3181
.3181019

 ( 1)  hotline - verdade = 0

       F(  1,   145) =    0.16
            Prob > F =    0.6935
.69345228

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   145) =    1.96
            Prob > F =    0.1229
.12292159

. 
. matrix define means=(m_bsfrelimo09_2, m_bsfrelimo09_3, m_bsrenamo09_2, m_bsrenamo09_3 \ t1_bsf
> relimo09_2, t1_bsfrelimo09_3, t1_bsrenamo09_2, t1_bsrenamo09_3 \ t2_bsfrelimo09_2, t2_bsfrelim
> o09_3, t2_bsrenamo09_2, t2_bsrenamo09_3 \ t3_bsfrelimo09_2, t3_bsfrelimo09_3, t3_bsrenamo09_2,
>  t3_bsrenamo09_3 \ t4_bsfrelimo09_2, t4_bsfrelimo09_3, t4_bsrenamo09_2, t4_bsrenamo09_3)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_ballots.xml") append sheet("out5"
> ) 


note: results saved to outputregs_ballots.xml

. xml_tab $list2, save("outputregs_ballots.xml") append sheet("out5 stats") 


note: results saved to outputregs_ballots.xml

. estimates clear

. 
. *******************************************
. *****  OA TABLES 5: LASSO ROBUSTNESS  *****
. *******************************************
. 
. set more off

. 
. global ea_all="count2009 schoolbuild policesta electricity water sewer health recreation templ
> e meetroom road"

. 
. lars bsturnoutpres09 $ea_all if v==1 & time==1, a(lasso)
NOTE: Deleting all matrices
          ade[12,11]
           mu[1,1]
        meanx[1,11]
           R2[1,12]
          RSS[1,12]
           r2[1,1]
          rss[1,1]
           cp[1,12]
        normx[1,11]
         beta[12,11]
        sbeta[12,11]
        error[1,1]

sbeta[12,11]
             c1          c2          c3          c4          c5          c6          c7
 r1           0           0           0           0           0           0           0
 r2  -.11250515           0           0           0           0           0           0
 r3  -.12417615           0           0           0           0           0           0
 r4  -.25620323           0           0           0           0           0           0
 r5  -.37172896           0           0   .09689274           0           0           0
 r6  -.47115885           0           0   .17312604           0           0   .02205558
 r7  -.50144117           0   .00703569   .19329498           0           0   .02640818
 r8  -.58353959  -.08698913   .02444017   .25773385           0           0   .04558234
 r9  -.61565221  -.11451198   .03021096   .27115529           0   .02412229   .05076622
r10  -.70272252  -.22391025   .03906603   .29688919           0   .09837581   .06303932
r11  -.71691403   -.2462266   .04179523   .30956638  -.03474586   .12905893   .06830344
r12  -.71763052  -.24732498   .04182445   .31025274   -.0364261   .13042039   .06854619

             c8          c9         c10         c11
 r1           0           0           0           0
 r2           0           0           0           0
 r3     .011671           0           0           0
 r4   .10328162           0   .10904646           0
 r5   .16814185           0   .15922941           0
 r6    .2190012           0   .20001956           0
 r7   .23398617           0   .21195687           0
 r8   .29124615           0   .24177598           0
 r9    .3120987           0   .24831078           0
r10    .3651966   .08305525   .27550851           0
r11    .3729885   .10304366   .28597941           0
r12   .37333457   .10403633   .28647172   .00037855

Algorithm is lasso

Cp, R-squared and Actions along the sequence of models

+----------------------------------------------+
| Step |      Cp     | R-square |  Action      |
|------+-------------+----------+--------------|
|    1 |    34.4800  |  0.0000  |              | 
|    2 |    30.1740  |  0.0326  | +count2009   | 
|    3 |    31.0398  |  0.0385  | +recreation  | 
|    4 |    19.4159  |  0.1089  | +meetroom    | 
|    5 |    11.8215  |  0.1585  | +electricity | 
|    6 |     7.8802  |  0.1892  | +health      | 
|    7 |     8.5283  |  0.1962  | +policesta   | 
|    8 |     6.6433 *|  0.2162  | +schoolbuild | 
|    9 |     7.7201  |  0.2210  | +sewer       | 
|   10 |     8.0799  |  0.2295  | +temple      | 
|   11 |    10.0002  |  0.2299  | +water       | 
|   12 |    12.0000  |  0.2299  | +road        | 
+----------------------------------------------+
* indicates the smallest value for Cp

The coefficient values for the minimum Cp

+----------------------------+
| Variable    |  Coefficient |
|-------------+--------------|
| count2009   |      -0.0165 |
| schoolbuild |      -0.0440 |
| policesta   |       0.0039 |
| electricity |       0.0408 |
| health      |       0.0074 |
| recreation  |       0.0505 |
| meetroom    |       0.0412 |
+----------------------------+

. global ea_lasso="count2009 schoolbuild schoolbuild_miss policesta policesta_miss electricity e
> lectricity_miss health health_miss recreation recreation_miss meetroom meetroom_miss"

. 
.         regress bsturnoutpres09 $treat $prov $ea_lasso if v==1 & time==1
note: schoolbuild omitted because of collinearity

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 18,   142) =    6.26
       Model |  1.42056583    18  .078920324           Prob > F      =  0.0000
    Residual |  1.79078799   142  .012611183           R-squared     =  0.4424
-------------+------------------------------           Adj R-squared =  0.3717
       Total |  3.21135383   160  .020070961           Root MSE      =   .1123

----------------------------------------------------------------------------------
 bsturnoutpres09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       civiceduc |   .0533762   .0253836     2.10   0.037     .0031975    .1035548
         hotline |   .0557355   .0254755     2.19   0.030     .0053753    .1060957
         verdade |   .0627951   .0264787     2.37   0.019     .0104517    .1151385
             pr1 |  -.0668381   .0292678    -2.28   0.024     -.124695   -.0089812
             pr2 |   .0398239   .0323804     1.23   0.221    -.0241859    .1038338
             pr3 |    .139404   .0292697     4.76   0.000     .0815434    .1972646
       count2009 |  -.0086659   .0038777    -2.23   0.027    -.0163313   -.0010005
     schoolbuild |          0  (omitted)
schoolbuild_miss |   .0933243   .2040253     0.46   0.648     -.309995    .4966437
       policesta |   .0281791   .0249284     1.13   0.260    -.0210997    .0774579
  policesta_miss |   -.062956   .1185095    -0.53   0.596    -.2972269    .1713149
     electricity |  -.0049693   .0268539    -0.19   0.853    -.0580544    .0481158
electricity_miss |   -.027378   .1190312    -0.23   0.818    -.2626802    .2079242
          health |   .0007514   .0248766     0.03   0.976    -.0484249    .0499277
     health_miss |    .100168    .081894     1.22   0.223     -.061721     .262057
      recreation |   .0372706   .0251974     1.48   0.141    -.0125399    .0870811
 recreation_miss |  -.0651108   .0651427    -1.00   0.319    -.1938857     .063664
        meetroom |   .0019354   .0236056     0.08   0.935    -.0447284    .0485993
   meetroom_miss |   .0468875   .0410152     1.14   0.255    -.0341919    .1279669
           _cons |   .4013853   .0354492    11.32   0.000     .3313088    .4714617
----------------------------------------------------------------------------------

.         estimates store l_1

.         sum bsturnoutpres09 if e(sample) & control == 1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsturnou~s09 |        41    .4400518    .1197301   .2245571     .73734

.         scalar define m_l_1=r(mean)

.         display m_l_1
.44005181

.         test civiceduc = hotline

 ( 1)  civiceduc - hotline = 0

       F(  1,   142) =    0.01
            Prob > F =    0.9264

.         scalar define t1_l_1=r(p)

.         display t1_l_1
.9264065

.         test civiceduc = verdade

 ( 1)  civiceduc - verdade = 0

       F(  1,   142) =    0.13
            Prob > F =    0.7150

.         scalar define t2_l_1=r(p)

.         display t2_l_1
.71500433

.         test hotline = verdade

 ( 1)  hotline - verdade = 0

       F(  1,   142) =    0.07
            Prob > F =    0.7903

.         scalar define t3_l_1=r(p)

.         display t3_l_1
.79027996

.         test civiceduc hotline verdade

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   142) =    2.51
            Prob > F =    0.0615

.         scalar define t4_l_1=r(p)

.         display t4_l_1
.06151128

. 
. lars bsturnoutparl09 $ea_all if v==1 & time==1, a(lasso)
NOTE: Deleting all matrices
          ade[12,11]
           mu[1,1]
        meanx[1,11]
           R2[1,12]
          RSS[1,12]
           r2[1,1]
          rss[1,1]
           cp[1,12]
        normx[1,11]
         beta[12,11]
        sbeta[12,11]
        error[1,1]

sbeta[12,11]
             c1          c2          c3          c4          c5          c6          c7
 r1           0           0           0           0           0           0           0
 r2  -.10988243           0           0           0           0           0           0
 r3  -.19186414           0           0           0           0           0           0
 r4  -.25320804           0           0           0           0           0           0
 r5  -.36046053           0           0   .08995388           0           0           0
 r6   -.5016655           0           0   .19821629           0           0   .03132214
 r7  -.59291572  -.09751536           0   .27830672           0           0   .05920021
 r8  -.59814664  -.10203518           0   .28089747           0   .00394315   .06036219
 r9  -.65735765  -.15278333   .01064051   .30564465           0   .04842115    .0699205
r10  -.66434489  -.15906664   .01367078   .30749356           0   .05614284   .07131835
r11  -.69096902  -.19356454   .02227891   .31177284           0   .08710431   .07596722
r12  -.71161404  -.22731814   .03119082   .32810662  -.05391393   .14028163     .084673

             c8          c9         c10         c11
 r1           0           0           0           0
 r2           0           0           0           0
 r3           0           0   .08198171           0
 r4   .04256515           0    .1326481           0
 r5   .10278049           0   .17923725           0
 r6    .1750082           0   .23716522           0
 r7   .24019633           0   .27161848           0
 r8   .24366721           0   .27274364           0
 r9   .28211628           0   .28479286           0
r10   .28747793           0   .28660243  -.00633405
r11   .30645471   .02555009   .29622818  -.02743887
r12   .31994374   .05537884   .31311475   -.0446684

Algorithm is lasso

Cp, R-squared and Actions along the sequence of models

+----------------------------------------------+
| Step |      Cp     | R-square |  Action      |
|------+-------------+----------+--------------|
|    1 |    34.0550  |  0.0000  |              | 
|    2 |    29.7712  |  0.0325  | +count2009   | 
|    3 |    24.2166  |  0.0717  | +meetroom    | 
|    4 |    20.2796  |  0.1024  | +recreation  | 
|    5 |    12.9495  |  0.1508  | +electricity | 
|    6 |     6.5749  |  0.1941  | +health      | 
|    7 |     4.2380 *|  0.2166  | +schoolbuild | 
|    8 |     6.0788  |  0.2174  | +sewer       | 
|    9 |     6.7524  |  0.2243  | +policesta   | 
|   10 |     8.6170  |  0.2250  | +road        | 
|   11 |    10.1972  |  0.2272  | +temple      | 
|   12 |    12.0000  |  0.2282  | +water       | 
+----------------------------------------------+
* indicates the smallest value for Cp

The coefficient values for the minimum Cp

+----------------------------+
| Variable    |  Coefficient |
|-------------+--------------|
| count2009   |      -0.0167 |
| schoolbuild |      -0.0494 |
| electricity |       0.0440 |
| health      |       0.0096 |
| recreation  |       0.0416 |
| meetroom    |       0.0463 |
+----------------------------+

. global ea_lasso="count2009 schoolbuild schoolbuild_miss electricity electricity_miss health he
> alth_miss recreation recreation_miss meetroom meetroom_miss"

. 
.         regress bsturnoutparl09 $treat $prov $ea_lasso if v==1 & time==1
note: schoolbuild omitted because of collinearity

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 16,   144) =    6.72
       Model |  1.34720307    16  .084200192           Prob > F      =  0.0000
    Residual |  1.80451982   144  .012531388           R-squared     =  0.4274
-------------+------------------------------           Adj R-squared =  0.3638
       Total |  3.15172289   160  .019698268           Root MSE      =  .11194

----------------------------------------------------------------------------------
 bsturnoutparl09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       civiceduc |   .0514001    .025302     2.03   0.044     .0013887    .1014114
         hotline |     .05613   .0253468     2.21   0.028     .0060301      .10623
         verdade |   .0599229   .0262381     2.28   0.024     .0080614    .1117845
             pr1 |  -.0736063   .0289274    -2.54   0.012    -.1307834   -.0164291
             pr2 |    .032423   .0308294     1.05   0.295    -.0285137    .0933597
             pr3 |   .1253451   .0287029     4.37   0.000     .0686116    .1820785
       count2009 |  -.0090794   .0038363    -2.37   0.019    -.0166621   -.0014967
     schoolbuild |          0  (omitted)
schoolbuild_miss |   .0766704   .1555665     0.49   0.623    -.2308185    .3841593
     electricity |   .0085106   .0235689     0.36   0.719    -.0380751    .0550963
electricity_miss |  -.0472294   .1183586    -0.40   0.690     -.281174    .1867152
          health |   .0147019   .0228926     0.64   0.522    -.0305472    .0599509
     health_miss |   .0978418   .0815406     1.20   0.232    -.0633294     .259013
      recreation |   .0258758   .0249938     1.04   0.302    -.0235263     .075278
 recreation_miss |  -.0652891   .0649104    -1.01   0.316    -.1935893    .0630112
        meetroom |   .0071761   .0233474     0.31   0.759    -.0389717     .053324
   meetroom_miss |   .0184095   .0407211     0.45   0.652    -.0620788    .0988978
           _cons |   .4158215   .0349307    11.90   0.000     .3467783    .4848648
----------------------------------------------------------------------------------

.         estimates store l_2

.         sum bsturnoutparl09 if e(sample) & control == 1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsturnou~l09 |        41    .4378275    .1162358   .2245571   .6983862

.         scalar define m_l_2=r(mean)

.         display m_l_2
.43782748

.         test civiceduc = hotline

 ( 1)  civiceduc - hotline = 0

       F(  1,   144) =    0.03
            Prob > F =    0.8523

.         scalar define t1_l_2=r(p)

.         display t1_l_2
.85227049

.         test civiceduc = verdade

 ( 1)  civiceduc - verdade = 0

       F(  1,   144) =    0.11
            Prob > F =    0.7385

.         scalar define t2_l_2=r(p)

.         display t2_l_2
.73854085

.         test hotline = verdade

 ( 1)  hotline - verdade = 0

       F(  1,   144) =    0.02
            Prob > F =    0.8853

.         scalar define t3_l_2=r(p)

.         display t3_l_2
.88531698

.         test civiceduc hotline verdade

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   144) =    2.40
            Prob > F =    0.0701

.         scalar define t4_l_2=r(p)

.         display t4_l_2
.07011083

. 
. *lars bsnullpres09 $ea_all if v==1 & time==1, a(lasso)
. *does not work, same as for bsturnoutpres09
. global ea_lasso="count2009 schoolbuild schoolbuild_miss policesta policesta_miss electricity e
> lectricity_miss health health_miss recreation recreation_miss meetroom meetroom_miss"

. 
.         regress bsnullpres09 $treat $prov $ea_lasso if v==1 & time==1
note: schoolbuild omitted because of collinearity

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 18,   142) =    2.92
       Model |  .013486447    18  .000749247           Prob > F      =  0.0002
    Residual |  .036383313   142  .000256221           R-squared     =  0.2704
-------------+------------------------------           Adj R-squared =  0.1780
       Total |   .04986976   160  .000311686           Root MSE      =  .01601

----------------------------------------------------------------------------------
    bsnullpres09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       civiceduc |  -.0036383   .0036181    -1.01   0.316    -.0107907     .003514
         hotline |  -.0044155   .0036312    -1.22   0.226    -.0115938    .0027627
         verdade |   -.006489   .0037742    -1.72   0.088    -.0139499    .0009719
             pr1 |   .0130869   .0041718     3.14   0.002     .0048401    .0213337
             pr2 |   .0002457   .0046154     0.05   0.958    -.0088781    .0093695
             pr3 |   -.005504    .004172    -1.32   0.189    -.0137513    .0027433
       count2009 |  -.0014495   .0005527    -2.62   0.010    -.0025421   -.0003569
     schoolbuild |          0  (omitted)
schoolbuild_miss |  -.0339722   .0290812    -1.17   0.245    -.0914603     .023516
       policesta |  -.0008707   .0035532    -0.25   0.807    -.0078948    .0061533
  policesta_miss |   .0067151    .016892     0.40   0.692    -.0266773    .0401075
     electricity |  -.0041879   .0038277    -1.09   0.276    -.0117546    .0033787
electricity_miss |   .0115345   .0169664     0.68   0.498    -.0220049    .0450739
          health |  -.0047834   .0035458    -1.35   0.179    -.0117928    .0022261
     health_miss |  -.0054333    .011673    -0.47   0.642    -.0285085     .017642
      recreation |   .0043472   .0035916     1.21   0.228    -.0027527     .011447
 recreation_miss |   .0073803   .0092853     0.79   0.428    -.0109749    .0257355
        meetroom |   .0008573   .0033647     0.25   0.799    -.0057941    .0075086
   meetroom_miss |   .0110698   .0058462     1.89   0.060    -.0004871    .0226266
           _cons |   .0420734   .0050528     8.33   0.000     .0320849     .052062
----------------------------------------------------------------------------------

.         estimates store l_3

.         sum bsnullpres09 if e(sample) & control == 1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsnullpres09 |        41    .0364498    .0262813   .0142119   .1372213

.         scalar define m_l_3=r(mean)

.         display m_l_3
.03644975

.         test civiceduc = hotline

 ( 1)  civiceduc - hotline = 0

       F(  1,   142) =    0.05
            Prob > F =    0.8310

.         scalar define t1_l_3=r(p)

.         display t1_l_3
.83097155

.         test civiceduc = verdade

 ( 1)  civiceduc - verdade = 0

       F(  1,   142) =    0.60
            Prob > F =    0.4385

.         scalar define t2_l_3=r(p)

.         display t2_l_3
.43853563

.         test hotline = verdade

 ( 1)  hotline - verdade = 0

       F(  1,   142) =    0.30
            Prob > F =    0.5838

.         scalar define t3_l_3=r(p)

.         display t3_l_3
.58384996

.         test civiceduc hotline verdade

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   142) =    1.05
            Prob > F =    0.3720

.         scalar define t4_l_3=r(p)

.         display t4_l_3
.37198719

. 
. *lars bsnullparl09 $ea_all if v==1 & time==1, a(lasso)
. *does not work, same as for bsturnoutparl09
. global ea_lasso="count2009 schoolbuild schoolbuild_miss electricity electricity_miss health he
> alth_miss recreation recreation_miss meetroom meetroom_miss"

. 
.         regress bsnullparl09 $treat $prov $ea_lasso if v==1 & time==1
note: schoolbuild omitted because of collinearity

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 16,   144) =    2.90
       Model |  .009938264    16  .000621141           Prob > F      =  0.0004
    Residual |  .030798264   144  .000213877           R-squared     =  0.2440
-------------+------------------------------           Adj R-squared =  0.1600
       Total |  .040736528   160  .000254603           Root MSE      =  .01462

----------------------------------------------------------------------------------
    bsnullparl09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       civiceduc |  -.0048147   .0033055    -1.46   0.147    -.0113483    .0017189
         hotline |  -.0028858   .0033114    -0.87   0.385     -.009431    .0036593
         verdade |  -.0014135   .0034278    -0.41   0.681    -.0081887    .0053618
             pr1 |    .005569   .0037791     1.47   0.143    -.0019007    .0130388
             pr2 |  -.0048126   .0040276    -1.19   0.234    -.0127735    .0031482
             pr3 |  -.0127369   .0037498    -3.40   0.001    -.0201487   -.0053252
       count2009 |  -.0012257   .0005012    -2.45   0.016    -.0022164   -.0002351
     schoolbuild |          0  (omitted)
schoolbuild_miss |  -.0227122   .0203235    -1.12   0.266    -.0628832    .0174587
     electricity |  -.0019435   .0030791    -0.63   0.529    -.0080296    .0041425
electricity_miss |   .0051772   .0154626     0.33   0.738    -.0253857    .0357402
          health |   .0015933   .0029907     0.53   0.595    -.0043181    .0075047
     health_miss |  -.0014563   .0106526    -0.14   0.891     -.022512    .0195994
      recreation |   .0042639   .0032652     1.31   0.194    -.0021901    .0107179
 recreation_miss |   .0068507     .00848     0.81   0.421    -.0099107    .0236121
        meetroom |  -.0012396   .0030501    -0.41   0.685    -.0072684    .0047892
   meetroom_miss |   .0080959   .0053199     1.52   0.130    -.0024192    .0186111
           _cons |   .0326866   .0045634     7.16   0.000     .0236666    .0417065
----------------------------------------------------------------------------------

.         estimates store l_4

.         sum bsnullparl09 if e(sample) & control == 1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsnullparl09 |        41    .0283779    .0209151   .0079787   .1172529

.         scalar define m_l_4=r(mean)

.         display m_l_4
.02837789

.         test civiceduc = hotline

 ( 1)  civiceduc - hotline = 0

       F(  1,   144) =    0.34
            Prob > F =    0.5612

.         scalar define t1_l_4=r(p)

.         display t1_l_4
.56124932

.         test civiceduc = verdade

 ( 1)  civiceduc - verdade = 0

       F(  1,   144) =    1.04
            Prob > F =    0.3087

.         scalar define t2_l_4=r(p)

.         display t2_l_4
.30868749

.         test hotline = verdade

 ( 1)  hotline - verdade = 0

       F(  1,   144) =    0.18
            Prob > F =    0.6683

.         scalar define t3_l_4=r(p)

.         display t3_l_4
.66832055

.         test civiceduc hotline verdade

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   144) =    0.79
            Prob > F =    0.5030

.         scalar define t4_l_4=r(p)

.         display t4_l_4
.50295496

. 
. lars bsblankpres09 $ea_all if v==1 & time==1, a(lasso)
NOTE: Deleting all matrices
          ade[12,11]
           mu[1,1]
        meanx[1,11]
           R2[1,12]
          RSS[1,12]
           r2[1,1]
          rss[1,1]
           cp[1,12]
        normx[1,11]
         beta[12,11]
        sbeta[12,11]
        error[1,1]

sbeta[12,11]
             c1          c2          c3          c4          c5          c6          c7
 r1           0           0           0           0           0           0           0
 r2           0           0           0  -.15581947           0           0           0
 r3           0           0           0  -.16825997   -.0124405           0           0
 r4   -.0078737           0           0  -.17467121  -.01809448           0           0
 r5  -.03269505           0           0   -.1912835   -.0296076           0           0
 r6  -.05379306           0           0  -.20719763  -.03143081  -.01656365           0
 r7  -.05609863           0           0  -.20815303  -.03126081   -.0185267   -.0023853
 r8  -.06327449           0           0  -.21310507  -.03365927  -.02150369  -.01122075
 r9  -.07496112           0           0  -.21629364   -.0374667  -.03275616  -.02586564
r10  -.08641705           0           0  -.21802173  -.03885945  -.04107826  -.04326161
r11  -.09540004  -.01838149           0  -.21778641  -.04172557  -.04385075  -.05359696
r12  -.10033697  -.02888707  -.00530162  -.21547923  -.04316899  -.04582815  -.05778819

             c8          c9         c10         c11
 r1           0           0           0           0
 r2           0           0           0           0
 r3           0           0           0           0
 r4           0           0           0           0
 r5           0           0  -.02584397           0
 r6           0           0  -.05169254           0
 r7           0           0  -.05402444           0
 r8           0    .0142272  -.05956864           0
 r9           0   .03256932  -.06818553   .02692622
r10   .01637232   .04695977  -.07991794   .04959716
r11   .03007694   .06461871  -.08661272   .06554374
r12   .03794842   .07486794  -.09010655   .07544775

Algorithm is lasso

Cp, R-squared and Actions along the sequence of models

+----------------------------------------------+
| Step |      Cp     | R-square |  Action      |
|------+-------------+----------+--------------|
|    1 |    57.6423  |  0.0000  |              | 
|    2 |    22.5072  |  0.1714  | +electricity | 
|    3 |    20.7449  |  0.1888  | +water       | 
|    4 |    20.0358  |  0.2013  | +count2009   | 
|    5 |    13.2510  |  0.2418  | +meetroom    | 
|    6 |     9.1650  |  0.2699  | +sewer       | 
|    7 |    10.6284  |  0.2724  | +health      | 
|    8 |    10.4586  |  0.2824  | +temple      | 
|    9 |     9.1291  |  0.2978  | +road        | 
|   10 |     9.0233 *|  0.3075  | +recreation  | 
|   11 |    10.1386  |  0.3116  | +schoolbuild | 
|   12 |    12.0000  |  0.3122  | +policesta   | 
+----------------------------------------------+
* indicates the smallest value for Cp

The coefficient values for the minimum Cp

+----------------------------+
| Variable    |  Coefficient |
|-------------+--------------|
| count2009   |      -0.0024 |
| electricity |      -0.0345 |
| water       |      -0.0068 |
| sewer       |      -0.0084 |
| health      |      -0.0070 |
| recreation  |       0.0028 |
| temple      |       0.0120 |
| meetroom    |      -0.0136 |
| road        |       0.0091 |
+----------------------------+

. global ea_lasso="count2009 electricity electricity_miss water water_miss sewer sewer_miss heal
> th health_miss recreation recreation_miss temple temple_miss meetroom meetroom_miss road road_
> miss"

. 
.         regress bsblankpres09 $treat $prov $ea_lasso if v==1 & time==1
note: sewer_miss omitted because of collinearity

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 22,   138) =    6.34
       Model |  .218575413    22  .009935246           Prob > F      =  0.0000
    Residual |  .216220628   138  .001566816           R-squared     =  0.5027
-------------+------------------------------           Adj R-squared =  0.4234
       Total |  .434796041   160  .002717475           Root MSE      =  .03958

----------------------------------------------------------------------------------
   bsblankpres09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       civiceduc |   .0005388   .0091018     0.06   0.953    -.0174582    .0185357
         hotline |   .0023704   .0090984     0.26   0.795    -.0156199    .0203606
         verdade |  -.0024024   .0094908    -0.25   0.801    -.0211687    .0163639
             pr1 |   .0131639    .010557     1.25   0.215    -.0077106    .0340384
             pr2 |  -.0380667   .0117302    -3.25   0.001    -.0612607   -.0148726
             pr3 |  -.0592982   .0108164    -5.48   0.000    -.0806854    -.037911
       count2009 |  -.0068314   .0014305    -4.78   0.000    -.0096598   -.0040029
     electricity |  -.0195132   .0090053    -2.17   0.032    -.0373193    -.001707
electricity_miss |  -.0112312   .0429775    -0.26   0.794    -.0962109    .0737485
           water |   .0063101   .0111255     0.57   0.572    -.0156884    .0283086
      water_miss |   .0914581   .0765049     1.20   0.234    -.0598153    .2427315
           sewer |  -.0015961   .0121485    -0.13   0.896    -.0256173    .0224252
      sewer_miss |          0  (omitted)
          health |  -.0027542   .0083433    -0.33   0.742    -.0192514     .013743
     health_miss |   .0010911   .0291169     0.04   0.970    -.0564819     .058664
      recreation |   .0098418   .0089426     1.10   0.273    -.0078404    .0275239
 recreation_miss |   .0002254   .0230841     0.01   0.992    -.0454189    .0458698
          temple |    .018258   .0132552     1.38   0.171    -.0079516    .0444676
     temple_miss |  -.0041542   .0320777    -0.13   0.897    -.0675815     .059273
        meetroom |  -.0020902   .0087394    -0.24   0.811    -.0193706    .0151902
   meetroom_miss |  -.0087543   .0145563    -0.60   0.549    -.0375366    .0200279
            road |  -.0000462    .008587    -0.01   0.996    -.0170252    .0169329
       road_miss |  -.0594742   .0424114    -1.40   0.163    -.1433344     .024386
           _cons |   .0956113   .0181089     5.28   0.000     .0598045    .1314181
----------------------------------------------------------------------------------

.         estimates store l_5

.         sum bsblankpres09 if e(sample) & control == 1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsblankpr~09 |        41    .0574556    .0541197          0   .2265813

.         scalar define m_l_5=r(mean)

.         display m_l_5
.0574556

.         test civiceduc = hotline

 ( 1)  civiceduc - hotline = 0

       F(  1,   138) =    0.04
            Prob > F =    0.8421

.         scalar define t1_l_5=r(p)

.         display t1_l_5
.84210212

.         test civiceduc = verdade

 ( 1)  civiceduc - verdade = 0

       F(  1,   138) =    0.10
            Prob > F =    0.7496

.         scalar define t2_l_5=r(p)

.         display t2_l_5
.74956896

.         test hotline = verdade

 ( 1)  hotline - verdade = 0

       F(  1,   138) =    0.25
            Prob > F =    0.6178

.         scalar define t3_l_5=r(p)

.         display t3_l_5
.61783777

.         test civiceduc hotline verdade

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   138) =    0.09
            Prob > F =    0.9677

.         scalar define t4_l_5=r(p)

.         display t4_l_5
.9677383

. 
. lars bsblankparl09 $ea_all if v==1 & time==1, a(lasso)
NOTE: Deleting all matrices
          ade[12,11]
           mu[1,1]
        meanx[1,11]
           R2[1,12]
          RSS[1,12]
           r2[1,1]
          rss[1,1]
           cp[1,12]
        normx[1,11]
         beta[12,11]
        sbeta[12,11]
        error[1,1]

sbeta[12,11]
             c1          c2          c3          c4          c5          c6          c7
 r1           0           0           0           0           0           0           0
 r2           0           0           0  -.27561823           0           0           0
 r3  -.04183141           0           0  -.31744963           0           0           0
 r4   -.0499135           0           0   -.3257575           0  -.00719553           0
 r5   -.0541771           0  -.00457959  -.32779107           0  -.01125071           0
 r6  -.06122862           0  -.01089617  -.33013626           0  -.01743914           0
 r7  -.06691549           0  -.01895921  -.32855262           0  -.02571201           0
 r8  -.06885313   .00345726  -.02228606  -.32841899           0  -.02896607           0
 r9  -.07554218   .02673752  -.03305212  -.32583936           0  -.04409379           0
r10  -.08381978   .06450193  -.04536117  -.32440672           0  -.06963871           0
r11  -.08524343   .06963998  -.04738782  -.32652055   .00931842  -.07724993           0
r12  -.08551621   .07058417  -.04763398   -.3268652   .01103209  -.07863124  -.00032207

             c8          c9         c10         c11
 r1           0           0           0           0
 r2           0           0           0           0
 r3           0           0           0           0
 r4           0           0           0           0
 r5           0           0           0           0
 r6           0           0  -.00814756           0
 r7           0           0  -.01405339   .01456112
 r8           0           0  -.01652779   .01999392
 r9           0  -.02120963  -.02855886   .03964265
r10   -.0219785  -.05067576  -.04049531   .06930738
r11   -.0242411  -.05554528  -.04347095   .07266377
r12  -.02458861  -.05642013  -.04398438   .07327314

Algorithm is lasso

Cp, R-squared and Actions along the sequence of models

+----------------------------------------------+
| Step |      Cp     | R-square |  Action      |
|------+-------------+----------+--------------|
|    1 |    49.8872  |  0.0000  |              | 
|    2 |     4.3200  |  0.2277  | +electricity | 
|    3 |     0.8245 *|  0.2540  | +count2009   | 
|    4 |     1.7936  |  0.2590  | +sewer       | 
|    5 |     3.2414  |  0.2616  | +policesta   | 
|    6 |     4.3231  |  0.2660  | +meetroom    | 
|    7 |     5.3390  |  0.2707  | +road        | 
|    8 |     6.9314  |  0.2727  | +schoolbuild | 
|    9 |     7.2397  |  0.2808  | +temple      | 
|   10 |     8.0369  |  0.2865  | +recreation  | 
|   11 |    10.0009  |  0.2867  | +water       | 
|   12 |    12.0000  |  0.2867  | +health      | 
+----------------------------------------------+
* indicates the smallest value for Cp

The coefficient values for the minimum Cp

+----------------------------+
| Variable    |  Coefficient |
|-------------+--------------|
| count2009   |      -0.0012 |
| electricity |      -0.0502 |
+----------------------------+

. global ea_lasso="count2009 electricity electricity_miss"

. 
.         regress bsblankparl09 $treat $prov $ea_lasso if v==1 & time==1

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  9,   151) =   12.28
       Model |  .272613675     9  .030290408           Prob > F      =  0.0000
    Residual |  .372420948   151  .002466364           R-squared     =  0.4226
-------------+------------------------------           Adj R-squared =  0.3882
       Total |  .645034623   160  .004031466           Root MSE      =  .04966

----------------------------------------------------------------------------------
   bsblankparl09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       civiceduc |   .0059099   .0109788     0.54   0.591     -.015782    .0276017
         hotline |   .0071209   .0111112     0.64   0.523    -.0148326    .0290744
         verdade |  -.0044813   .0111404    -0.40   0.688    -.0264925    .0175299
             pr1 |  -.0001429   .0113868    -0.01   0.990    -.0226408    .0223551
             pr2 |  -.0235744   .0123045    -1.92   0.057    -.0478857    .0007368
             pr3 |  -.0666546   .0122513    -5.44   0.000    -.0908607   -.0424485
       count2009 |  -.0060602   .0016168    -3.75   0.000    -.0092546   -.0028658
     electricity |  -.0391677   .0097593    -4.01   0.000      -.05845   -.0198854
electricity_miss |  -.0315727   .0232343    -1.36   0.176     -.077479    .0143337
           _cons |   .1492636   .0117915    12.66   0.000     .1259661    .1725611
----------------------------------------------------------------------------------

.         estimates store l_6

.         sum bsblankparl09 if e(sample) & control == 1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsblankpa~09 |        41    .0808875    .0600231          0   .2608696

.         scalar define m_l_6=r(mean)

.         display m_l_6
.08088748

.         test civiceduc = hotline

 ( 1)  civiceduc - hotline = 0

       F(  1,   151) =    0.01
            Prob > F =    0.9132

.         scalar define t1_l_6=r(p)

.         display t1_l_6
.91317557

.         test civiceduc = verdade

 ( 1)  civiceduc - verdade = 0

       F(  1,   151) =    0.87
            Prob > F =    0.3521

.         scalar define t2_l_6=r(p)

.         display t2_l_6
.35208766

.         test hotline = verdade

 ( 1)  hotline - verdade = 0

       F(  1,   151) =    1.06
            Prob > F =    0.3047

.         scalar define t3_l_6=r(p)

.         display t3_l_6
.30470057

.         test civiceduc hotline verdade

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   151) =    0.46
            Prob > F =    0.7081

.         scalar define t4_l_6=r(p)

.         display t4_l_6
.70810626

. 
. lars bsguebas09 $ea_all if v==1 & time==1, a(lasso)
NOTE: Deleting all matrices
          ade[12,11]
           mu[1,1]
        meanx[1,11]
           R2[1,12]
          RSS[1,12]
           r2[1,1]
          rss[1,1]
           cp[1,12]
        normx[1,11]
         beta[12,11]
        sbeta[12,11]
        error[1,1]

sbeta[12,11]
             c1          c2          c3          c4          c5          c6          c7
 r1           0           0           0           0           0           0           0
 r2           0           0           0   .04754614           0           0           0
 r3           0           0           0   .34184184           0           0           0
 r4           0           0           0   .35785284           0           0   .01582245
 r5  -.25778336           0           0   .53979757           0           0   .11028834
 r6  -.33825117           0           0   .60149256           0           0   .12813774
 r7   -.4005107           0           0   .65681361           0           0   .15142606
 r8  -.40429265           0           0   .66089026           0           0   .15319752
 r9  -.41636548           0           0   .66711751           0   .01273155    .1574153
r10  -.47459903           0   -.0949334   .73777229           0   .06992699   .20728073
r11  -.48826083           0  -.11684251   .75257741    .0059929   .08015939   .21820729
r12  -.52208895   .02863028  -.17328026   .78897786   .02482269   .10315445   .24499842

             c8          c9         c10         c11
 r1           0           0           0           0
 r2           0           0           0           0
 r3           0           0    .2942957           0
 r4           0           0   .31267099           0
 r5           0           0   .44715819           0
 r6   .04116006           0   .48016933           0
 r7   .08133529           0   .51526445  -.05641022
 r8     .084359  -.00437631   .51680227  -.06007956
 r9   .09326548    -.013808    .5195487  -.07196059
r10   .14047169  -.05597963   .53823333    -.114258
r11   .15169407  -.06629077   .54158648   -.1240537
r12   .17666139  -.10643777   .54883994  -.15191634

Algorithm is lasso

Cp, R-squared and Actions along the sequence of models

+----------------------------------------------+
| Step |      Cp     | R-square |  Action      |
|------+-------------+----------+--------------|
|    1 |    66.8728  |  0.0000  |              | 
|    2 |    64.5993  |  0.0189  | +electricity | 
|    3 |    27.9780  |  0.1899  | +meetroom    | 
|    4 |    27.8424  |  0.1994  | +health      | 
|    5 |     9.2462  |  0.2905  | +count2009   | 
|    6 |     7.1538  |  0.3087  | +recreation  | 
|    7 |     6.0354 *|  0.3225  | +road        | 
|    8 |     7.8489  |  0.3233  | +temple      | 
|    9 |     9.3159  |  0.3257  | +sewer       | 
|   10 |     8.7223  |  0.3371  | +policesta   | 
|   11 |    10.3962  |  0.3386  | +water       | 
|   12 |    12.0000  |  0.3403  | +schoolbuild | 
+----------------------------------------------+
* indicates the smallest value for Cp

The coefficient values for the minimum Cp

+----------------------------+
| Variable    |  Coefficient |
|-------------+--------------|
| count2009   |      -0.0113 |
| electricity |       0.1039 |
| health      |       0.0246 |
| recreation  |       0.0141 |
| meetroom    |       0.0878 |
| road        |      -0.0104 |
+----------------------------+

. global ea_lasso="count2009 electricity electricity_miss health health_miss recreation recreati
> on_miss meetroom meetroom_miss road road_miss"

. 
.         regress bsguebas09 $treat $prov $ea_lasso if v==1 & time==1

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 17,   143) =   20.46
       Model |  2.77678871    17  .163340513           Prob > F      =  0.0000
    Residual |  1.14169616   143  .007983889           R-squared     =  0.7086
-------------+------------------------------           Adj R-squared =  0.6740
       Total |  3.91848487   160   .02449053           Root MSE      =  .08935

----------------------------------------------------------------------------------
      bsguebas09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       civiceduc |   .0479259   .0203418     2.36   0.020     .0077164    .0881353
         hotline |   .0189641   .0202463     0.94   0.351    -.0210566    .0589847
         verdade |   .0428792    .021006     2.04   0.043     .0013568    .0844015
             pr1 |  -.1233878   .0231628    -5.33   0.000    -.1691735   -.0776022
             pr2 |   .1012731   .0248639     4.07   0.000     .0521249    .1504213
             pr3 |   .1894257   .0242548     7.81   0.000     .1414814      .23737
       count2009 |   .0005172    .003075     0.17   0.867     -.005561    .0065955
     electricity |   .0400233   .0190477     2.10   0.037     .0023718    .0776749
electricity_miss |  -.0332602   .0754645    -0.44   0.660    -.1824303    .1159098
          health |    .016384   .0182503     0.90   0.371    -.0196912    .0524593
     health_miss |   .0311485   .0622989     0.50   0.618    -.0919973    .1542942
      recreation |   .0025742   .0201085     0.13   0.898     -.037174    .0423225
 recreation_miss |  -.0307918   .0517262    -0.60   0.553    -.1330387     .071455
        meetroom |   .0149685   .0187646     0.80   0.426    -.0221233    .0520603
   meetroom_miss |  -.0164789   .0316527    -0.52   0.603    -.0790467    .0460888
            road |   .0099096   .0187024     0.53   0.597    -.0270591    .0468784
       road_miss |   .0457669   .0747602     0.61   0.541     -.102011    .1935447
           _cons |   .6410884   .0279684    22.92   0.000     .5858034    .6963733
----------------------------------------------------------------------------------

.         estimates store l_7

.         sum bsguebas09 if e(sample) & control == 1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  bsguebas09 |        41    .7228651     .184084   .1590538   .9491262

.         scalar define m_l_7=r(mean)

.         display m_l_7
.72286514

.         test civiceduc = hotline

 ( 1)  civiceduc - hotline = 0

       F(  1,   143) =    2.02
            Prob > F =    0.1576

.         scalar define t1_l_7=r(p)

.         display t1_l_7
.15758816

.         test civiceduc = verdade

 ( 1)  civiceduc - verdade = 0

       F(  1,   143) =    0.06
            Prob > F =    0.8075

.         scalar define t2_l_7=r(p)

.         display t2_l_7
.80750029

.         test hotline = verdade

 ( 1)  hotline - verdade = 0

       F(  1,   143) =    1.29
            Prob > F =    0.2586

.         scalar define t3_l_7=r(p)

.         display t3_l_7
.25863878

.         test civiceduc hotline verdade

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   143) =    2.30
            Prob > F =    0.0798

.         scalar define t4_l_7=r(p)

.         display t4_l_7
.07982206

. 
. lars bsdhlakama09 $ea_all if v==1 & time==1, a(lasso)
NOTE: Deleting all matrices
          ade[12,11]
           mu[1,1]
        meanx[1,11]
           R2[1,12]
          RSS[1,12]
           r2[1,1]
          rss[1,1]
           cp[1,12]
        normx[1,11]
         beta[12,11]
        sbeta[12,11]
        error[1,1]

sbeta[12,11]
             c1          c2          c3          c4          c5          c6          c7
 r1           0           0           0           0           0           0           0
 r2           0           0           0  -.08255113           0           0           0
 r3           0           0           0  -.21766018           0           0           0
 r4   .02152112           0           0  -.23535832           0           0           0
 r5    .0657799           0           0  -.26659638           0           0  -.01621883
 r6   .10409483           0           0  -.29597258           0           0  -.02471786
 r7   .13834201           0           0  -.31126154  -.02061814           0   -.0290683
 r8   .18164175  -.01362925           0  -.32913526  -.04676326           0  -.03348919
 r9   .29311077  -.04093037           0  -.36720511  -.06366966  -.07422766  -.04678316
r10   .37935513  -.06315363    .1533552  -.45942676  -.08558858  -.12929402  -.10734362
r11    .4219944  -.09408257   .22798396  -.50871584   -.1062495  -.14680305  -.13781275
r12   .42498973   -.0959682   .23342074  -.51236793  -.10752986  -.14739443  -.13976949

             c8          c9         c10         c11
 r1           0           0           0           0
 r2           0           0           0           0
 r3           0           0  -.13510905           0
 r4           0           0  -.14766673           0
 r5           0           0  -.17075681           0
 r6  -.01959845           0  -.18647514           0
 r7    -.037424           0  -.19489546           0
 r8  -.05737509           0  -.20593717           0
 r9  -.10827897           0  -.23127075           0
r10  -.15862581           0  -.25960811           0
r11  -.18543598   .04387522  -.26727038           0
r12     -.18706   .04658293  -.26772625  -.00195439

Algorithm is lasso

Cp, R-squared and Actions along the sequence of models

+----------------------------------------------+
| Step |      Cp     | R-square |  Action      |
|------+-------------+----------+--------------|
|    1 |    57.2357  |  0.0000  |              | 
|    2 |    50.5863  |  0.0400  | +electricity | 
|    3 |    32.0064  |  0.1352  | +meetroom    | 
|    4 |    31.1481  |  0.1484  | +count2009   | 
|    5 |    27.3908  |  0.1750  | +health      | 
|    6 |    24.5067  |  0.1976  | +recreation  | 
|    7 |    22.6215  |  0.2156  | +water       | 
|    8 |    20.0825  |  0.2366  | +schoolbuild | 
|    9 |    13.6287  |  0.2757  | +sewer       | 
|   10 |     9.0919 *|  0.3059  | +policesta   | 
|   11 |    10.0045  |  0.3109  | +temple      | 
|   12 |    12.0000  |  0.3109  | +road        | 
+----------------------------------------------+
* indicates the smallest value for Cp

The coefficient values for the minimum Cp

+----------------------------+
| Variable    |  Coefficient |
|-------------+--------------|
| count2009   |       0.0107 |
| schoolbuild |      -0.0320 |
| policesta   |       0.0242 |
| electricity |      -0.0727 |
| water       |      -0.0149 |
| sewer       |      -0.0265 |
| health      |      -0.0174 |
| recreation  |      -0.0275 |
| meetroom    |      -0.0442 |
+----------------------------+

. global ea_lasso="count2009 schoolbuild schoolbuild_miss policesta policesta_miss electricity e
> lectricity_miss water water_miss sewer sewer_miss health health_miss recreation recreation_mis
> s meetroom meetroom_miss"

. 
.         regress bsdhlakama09 $treat $prov $ea_lasso if v==1 & time==1
note: schoolbuild omitted because of collinearity
note: schoolbuild_miss omitted because of collinearity
note: sewer_miss omitted because of collinearity

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 20,   140) =   11.38
       Model |  1.18812394    20  .059406197           Prob > F      =  0.0000
    Residual |  .731088465   140   .00522206           R-squared     =  0.6191
-------------+------------------------------           Adj R-squared =  0.5646
       Total |   1.9192124   160  .011995078           Root MSE      =  .07226

----------------------------------------------------------------------------------
    bsdhlakama09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       civiceduc |  -.0298158   .0163915    -1.82   0.071    -.0622226    .0025911
         hotline |  -.0051152   .0166156    -0.31   0.759    -.0379652    .0277348
         verdade |  -.0173384   .0170735    -1.02   0.312    -.0510935    .0164167
             pr1 |     .08857    .018983     4.67   0.000     .0510396    .1261004
             pr2 |  -.0886143   .0218353    -4.06   0.000    -.1317839   -.0454447
             pr3 |  -.0974834   .0189256    -5.15   0.000    -.1349004   -.0600665
       count2009 |   .0011985   .0026051     0.46   0.646    -.0039519    .0063489
     schoolbuild |          0  (omitted)
schoolbuild_miss |          0  (omitted)
       policesta |   .0059863   .0161766     0.37   0.712    -.0259957    .0379683
  policesta_miss |   .0163997   .0782371     0.21   0.834    -.1382792    .1710786
     electricity |  -.0383031   .0178849    -2.14   0.034    -.0736626   -.0029436
electricity_miss |  -.0251159   .0779697    -0.32   0.748    -.1792662    .1290344
           water |   .0224783   .0196931     1.14   0.256     -.016456    .0614126
      water_miss |   .0599158   .1329848     0.45   0.653    -.2030023    .3228338
           sewer |  -.0037515   .0217861    -0.17   0.864    -.0468238    .0393208
      sewer_miss |          0  (omitted)
          health |  -.0036382   .0160492    -0.23   0.821    -.0353683    .0280919
     health_miss |  -.0124311   .0528974    -0.24   0.815    -.1170122      .09215
      recreation |  -.0255045   .0162375    -1.57   0.119    -.0576069     .006598
 recreation_miss |   .0123982   .0420746     0.29   0.769    -.0707856    .0955819
        meetroom |  -.0079081   .0155748    -0.51   0.612    -.0387002    .0228841
   meetroom_miss |  -.0067734   .0265045    -0.26   0.799    -.0591743    .0456274
           _cons |   .1654003   .0228674     7.23   0.000     .1201902    .2106104
----------------------------------------------------------------------------------

.         estimates store l_8

.         sum bsdhlakama09 if e(sample) & control == 1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsdhlakama09 |        41    .1139558    .1327448   .0017921   .5856444

.         scalar define m_l_8=r(mean)

.         display m_l_8
.11395576

.         test civiceduc = hotline

 ( 1)  civiceduc - hotline = 0

       F(  1,   140) =    2.21
            Prob > F =    0.1396

.         scalar define t1_l_8=r(p)

.         display t1_l_8
.13955833

.         test civiceduc = verdade

 ( 1)  civiceduc - verdade = 0

       F(  1,   140) =    0.57
            Prob > F =    0.4527

.         scalar define t2_l_8=r(p)

.         display t2_l_8
.4526955

.         test hotline = verdade

 ( 1)  hotline - verdade = 0

       F(  1,   140) =    0.50
            Prob > F =    0.4791

.         scalar define t3_l_8=r(p)

.         display t3_l_8
.47907622

.         test civiceduc hotline verdade

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   140) =    1.29
            Prob > F =    0.2804

.         scalar define t4_l_8=r(p)

.         display t4_l_8
.28041192

. 
. lars bssimango09 $ea_all if v==1 & time==1, a(lasso)
NOTE: Deleting all matrices
          ade[12,11]
           mu[1,1]
        meanx[1,11]
           R2[1,12]
          RSS[1,12]
           r2[1,1]
          rss[1,1]
           cp[1,12]
        normx[1,11]
         beta[12,11]
        sbeta[12,11]
        error[1,1]

sbeta[12,11]
             c1          c2          c3          c4          c5          c6          c7
 r1           0           0           0           0           0           0           0
 r2   .03892636           0           0           0           0           0           0
 r3   .12178998           0           0           0           0   .08286362           0
 r4   .14727099           0           0           0           0    .1113918           0
 r5   .16857526           0           0           0   .03038647   .12186937           0
 r6   .17570756     .009927           0           0    .0392091   .12479131           0
 r7   .18255984   .02002779           0           0   .04848806   .12585928           0
 r8   .18424293   .02242942  -.00359571           0   .05145654   .12563841           0
 r9   .18439835   .02268012  -.00386253           0   .05176604   .12560565  -.00017956
r10     .200443   .05001689  -.02966583           0   .08017594    .1202419  -.01454448
r11   .20777947   .06887955  -.03887781           0   .09590385   .11466279  -.02057665
r12     .216363   .08807317  -.04246087  -.01346679   .11477254   .11047847  -.02525097

             c8          c9         c10         c11
 r1           0           0           0           0
 r2           0           0           0           0
 r3           0           0           0           0
 r4           0           0  -.03974357           0
 r5           0           0  -.08423304           0
 r6           0           0  -.09898481           0
 r7           0           0  -.11526645   .00742822
 r8           0           0  -.11803936   .00960515
 r9           0           0  -.11828464    .0098175
r10  -.01468357           0  -.13909496   .03177898
r11  -.02034585   -.0160583  -.15024237   .03962795
r12  -.02673255  -.03117962   -.1603133   .04537986

Algorithm is lasso

Cp, R-squared and Actions along the sequence of models

+----------------------------------------------+
| Step |      Cp     | R-square |  Action      |
|------+-------------+----------+--------------|
|    1 |    97.4167  |  0.0000  |              | 
|    2 |    83.6150  |  0.0616  | +count2009   | 
|    3 |    38.8238  |  0.2441  | +sewer       | 
|    4 |    25.3843  |  0.3043  | +meetroom    | 
|    5 |    15.1068  |  0.3522  | +water       | 
|    6 |    13.4814  |  0.3663  | +schoolbuild | 
|    7 |    12.0340  |  0.3798  | +road        | 
|    8 |    13.1438  |  0.3833  | +policesta   | 
|    9 |    15.0548  |  0.3836  | +health      | 
|   10 |    10.3828 *|  0.4096  | +recreation  | 
|   11 |    10.6340  |  0.4164  | +temple      | 
|   12 |    12.0000  |  0.4189  | +electricity | 
+----------------------------------------------+
* indicates the smallest value for Cp

The coefficient values for the minimum Cp

+----------------------------+
| Variable    |  Coefficient |
|-------------+--------------|
| count2009   |       0.0057 |
| schoolbuild |       0.0253 |
| policesta   |      -0.0047 |
| water       |       0.0140 |
| sewer       |       0.0247 |
| health      |      -0.0024 |
| recreation  |      -0.0025 |
| meetroom    |      -0.0237 |
| road        |       0.0058 |
+----------------------------+

. global ea_lasso="count2009 schoolbuild schoolbuild_miss policesta policesta_miss water water_m
> iss sewer sewer_miss health health_miss recreation recreation_miss meetroom meetroom_miss road
>  road_miss"

. 
.         regress bssimango09 $treat $prov $ea_lasso if v==1 & time==1
note: schoolbuild omitted because of collinearity
note: schoolbuild_miss omitted because of collinearity
note: sewer_miss omitted because of collinearity

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 20,   140) =    7.46
       Model |  .164929774    20  .008246489           Prob > F      =  0.0000
    Residual |  .154656983   140  .001104693           R-squared     =  0.5161
-------------+------------------------------           Adj R-squared =  0.4469
       Total |  .319586757   160  .001997417           Root MSE      =  .03324

----------------------------------------------------------------------------------
     bssimango09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       civiceduc |   -.012604   .0075829    -1.66   0.099    -.0275958    .0023877
         hotline |  -.0015825   .0075249    -0.21   0.834    -.0164595    .0132946
         verdade |  -.0162956   .0078037    -2.09   0.039    -.0317239   -.0008674
             pr1 |    .017118   .0086475     1.98   0.050     .0000214    .0342146
             pr2 |   .0104493   .0096171     1.09   0.279    -.0085643    .0294629
             pr3 |  -.0211861   .0086065    -2.46   0.015    -.0382015   -.0041706
       count2009 |   .0041994   .0011848     3.54   0.001     .0018569    .0065419
     schoolbuild |          0  (omitted)
schoolbuild_miss |          0  (omitted)
       policesta |  -.0037488    .006801    -0.55   0.582    -.0171947    .0096971
  policesta_miss |   .0129221   .0359919     0.36   0.720    -.0582358    .0840801
           water |    .024248   .0090674     2.67   0.008     .0063212    .0421748
      water_miss |  -.0153156   .0606093    -0.25   0.801    -.1351434    .1045122
           sewer |   .0203766     .01018     2.00   0.047     .0002501     .040503
      sewer_miss |          0  (omitted)
          health |  -.0088548   .0072777    -1.22   0.226    -.0232432    .0055336
     health_miss |  -.0310909   .0244032    -1.27   0.205    -.0793374    .0171555
      recreation |   .0085333   .0075144     1.14   0.258    -.0063231    .0233897
 recreation_miss |   .0201828   .0193396     1.04   0.298    -.0180526    .0584181
        meetroom |  -.0174673   .0072532    -2.41   0.017    -.0318073   -.0031272
   meetroom_miss |   .0090054   .0122306     0.74   0.463    -.0151753     .033186
            road |   .0002783   .0073277     0.04   0.970    -.0142089    .0147655
       road_miss |  -.0239573   .0354376    -0.68   0.500    -.0940194    .0461048
           _cons |   .0446982   .0104678     4.27   0.000     .0240029    .0653936
----------------------------------------------------------------------------------

.         estimates store l_9

.         sum bssimango09 if e(sample) & control == 1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 bssimango09 |        41    .0692737    .0559971   .0069903   .2279817

.         scalar define m_l_9=r(mean)

.         display m_l_9
.06927374

.         test civiceduc = hotline

 ( 1)  civiceduc - hotline = 0

       F(  1,   140) =    2.09
            Prob > F =    0.1505

.         scalar define t1_l_9=r(p)

.         display t1_l_9
.15051015

.         test civiceduc = verdade

 ( 1)  civiceduc - verdade = 0

       F(  1,   140) =    0.23
            Prob > F =    0.6330

.         scalar define t2_l_9=r(p)

.         display t2_l_9
.63302657

.         test hotline = verdade

 ( 1)  hotline - verdade = 0

       F(  1,   140) =    3.46
            Prob > F =    0.0649

.         scalar define t3_l_9=r(p)

.         display t3_l_9
.0648741

.         test civiceduc hotline verdade

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   140) =    2.09
            Prob > F =    0.1044

.         scalar define t4_l_9=r(p)

.         display t4_l_9
.10442889

. 
. lars bsfrelimo09 $ea_all if v==1 & time==1, a(lasso)
NOTE: Deleting all matrices
          ade[12,11]
           mu[1,1]
        meanx[1,11]
           R2[1,12]
          RSS[1,12]
           r2[1,1]
          rss[1,1]
           cp[1,12]
        normx[1,11]
         beta[12,11]
        sbeta[12,11]
        error[1,1]

sbeta[12,11]
             c1          c2          c3          c4          c5          c6          c7
 r1           0           0           0           0           0           0           0
 r2           0           0           0   .30803783           0           0           0
 r3           0           0           0   .55520398           0           0           0
 r4           0           0           0    .5899196           0           0    .0343068
 r5   -.0643703           0           0   .63535247           0           0   .05789559
 r6  -.26680833           0           0   .79056251           0           0   .10280047
 r7  -.27878329           0           0   .80120292           0           0   .10727973
 r8  -.37747605           0           0   .83653189           0     .110313   .13495013
 r9  -.38230846           0  -.00816681   .84181367           0   .11532862   .13886982
r10  -.41364228           0  -.06064697   .87340127   .00866345    .1432914   .16313175
r11  -.45833352  -.04222145  -.13023868   .92099452    .0197662   .18253138   .19885385
r12   -.4600761  -.04470617  -.13303448   .92270241   .01974879   .18442491   .20020205

             c8          c9         c10         c11
 r1           0           0           0           0
 r2           0           0           0           0
 r3           0           0   .24716615           0
 r4           0           0   .28700812           0
 r5           0           0   .32059051           0
 r6   .10354901           0   .40363875           0
 r7   .11127628           0   .41038892   -.0108499
 r8   .17373974           0   .44194525  -.10390393
 r9   .17720179           0   .44392436  -.10718398
r10     .199785           0   .45534163  -.12822834
r11    .2390309           0   .46977692  -.15422027
r12   .24043668   .00197988   .47061505  -.15503475

Algorithm is lasso

Cp, R-squared and Actions along the sequence of models

+----------------------------------------------+
| Step |      Cp     | R-square |  Action      |
|------+-------------+----------+--------------|
|    1 |    75.4535  |  0.0000  |              | 
|    2 |    48.5518  |  0.1233  | +electricity | 
|    3 |    21.7287  |  0.2462  | +meetroom    | 
|    4 |    19.6964  |  0.2634  | +health      | 
|    5 |    16.4914  |  0.2856  | +count2009   | 
|    6 |     6.2406  |  0.3379  | +recreation  | 
|    7 |     7.5915  |  0.3406  | +road        | 
|    8 |     5.6513 *|  0.3574  | +sewer       | 
|    9 |     7.4657  |  0.3582  | +policesta   | 
|   10 |     8.5537  |  0.3621  | +water       | 
|   11 |    10.0009  |  0.3645  | +schoolbuild | 
|   12 |    12.0000  |  0.3645  | +temple      | 
+----------------------------------------------+
* indicates the smallest value for Cp

The coefficient values for the minimum Cp

+----------------------------+
| Variable    |  Coefficient |
|-------------+--------------|
| count2009   |      -0.0107 |
| electricity |       0.1323 |
| sewer       |       0.0226 |
| health      |       0.0219 |
| recreation  |       0.0301 |
| meetroom    |       0.0753 |
| road        |      -0.0191 |
+----------------------------+

. global ea_lasso="count2009 electricity electricity_miss sewer sewer_miss health health_miss re
> creation recreation_miss meetroom meetroom_miss road road_miss"

. 
.         regress bsfrelimo09 $treat $prov $ea_lasso if v==1 & time==1

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 19,   141) =   18.71
       Model |  2.93824118    19  .154644273           Prob > F      =  0.0000
    Residual |  1.16525204   141  .008264199           R-squared     =  0.7160
-------------+------------------------------           Adj R-squared =  0.6778
       Total |  4.10349322   160  .025646833           Root MSE      =  .09091

----------------------------------------------------------------------------------
     bsfrelimo09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       civiceduc |   .0378023   .0207501     1.82   0.071    -.0032191    .0788238
         hotline |   .0105454   .0207065     0.51   0.611      -.03039    .0514808
         verdade |   .0300535   .0214867     1.40   0.164    -.0124242    .0725313
             pr1 |  -.1069601   .0238963    -4.48   0.000    -.1542014   -.0597188
             pr2 |   .1004456   .0261641     3.84   0.000     .0487209    .1521703
             pr3 |   .2144851   .0248016     8.65   0.000     .1654541    .2635161
       count2009 |   .0038225   .0032349     1.18   0.239    -.0025726    .0102176
     electricity |   .0733605   .0197422     3.72   0.000     .0343316    .1123895
electricity_miss |   .0513887   .0981394     0.52   0.601    -.1426262    .2454037
           sewer |   -.015251    .025353    -0.60   0.548    -.0653721      .03487
      sewer_miss |  -.0993978   .1598443    -0.62   0.535    -.4153989    .2166034
          health |   .0032269   .0187352     0.17   0.863    -.0338114    .0402652
     health_miss |   .0195757   .0666705     0.29   0.769    -.1122273    .1513788
      recreation |   .0098177    .020466     0.48   0.632    -.0306423    .0502776
 recreation_miss |  -.0243305   .0527684    -0.46   0.645    -.1286499    .0799889
        meetroom |   .0020859     .01936     0.11   0.914    -.0361875    .0403593
   meetroom_miss |   .0057943   .0332643     0.17   0.862    -.0599669    .0715555
            road |   .0231326   .0196713     1.18   0.242    -.0157562    .0620214
       road_miss |    .055768   .0952723     0.59   0.559    -.1325788    .2441149
           _cons |   .6047804   .0285237    21.20   0.000      .548391    .6611699
----------------------------------------------------------------------------------

.         estimates store l_10

.         sum bsfrelimo09 if e(sample) & control == 1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 bsfrelimo09 |        41    .7218491    .1881129   .1647635   .9476358

.         scalar define m_l_10=r(mean)

.         display m_l_10
.7218491

.         test civiceduc = hotline

 ( 1)  civiceduc - hotline = 0

       F(  1,   141) =    1.72
            Prob > F =    0.1917

.         scalar define t1_l_10=r(p)

.         display t1_l_10
.19172005

.         test civiceduc = verdade

 ( 1)  civiceduc - verdade = 0

       F(  1,   141) =    0.14
            Prob > F =    0.7135

.         scalar define t2_l_10=r(p)

.         display t2_l_10
.71353746

.         test hotline = verdade

 ( 1)  hotline - verdade = 0

       F(  1,   141) =    0.83
            Prob > F =    0.3648

.         scalar define t3_l_10=r(p)

.         display t3_l_10
.36478802

.         test civiceduc hotline verdade

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   141) =    1.36
            Prob > F =    0.2584

.         scalar define t4_l_10=r(p)

.         display t4_l_10
.25842604

. 
. lars bsrenamo09 $ea_all if v==1 & time==1, a(lasso)
NOTE: Deleting all matrices
          ade[12,11]
           mu[1,1]
        meanx[1,11]
           R2[1,12]
          RSS[1,12]
           r2[1,1]
          rss[1,1]
           cp[1,12]
        normx[1,11]
         beta[12,11]
        sbeta[12,11]
        error[1,1]

sbeta[12,11]
             c1          c2          c3          c4          c5          c6          c7
 r1           0           0           0           0           0           0           0
 r2           0           0           0           0           0           0           0
 r3           0           0           0  -.06156422           0           0           0
 r4   .06810452           0           0  -.11757076           0           0           0
 r5   .17395943           0           0  -.19228366           0           0  -.03879101
 r6   .35425739           0           0  -.33051882           0           0  -.07878477
 r7    .3882176           0           0  -.34567967  -.02044537           0  -.08309875
 r8   .43288713           0           0  -.36208152  -.02702488  -.02994265  -.08929609
 r9   .45103471           0   .03164089  -.38171907  -.03151739  -.04162355  -.10220481
r10   .52386752   -.0187674   .16114839  -.45959967  -.05002778  -.08812675  -.15334774
r11   .53999564  -.03046614   .18937639  -.47824304  -.05784268  -.09474946  -.16487255
r12   .56011813  -.04749672   .22294811  -.49989567  -.06904348  -.10842796  -.18080912

             c8          c9         c10         c11
 r1           0           0           0           0
 r2           0           0  -.06022834           0
 r3           0           0  -.12179256           0
 r4           0           0  -.16153188           0
 r5           0           0  -.21675705           0
 r6  -.09222415           0  -.29072254           0
 r7  -.10990033           0  -.29907231           0
 r8  -.13237024           0  -.30889915           0
 r9  -.14380365           0  -.31468725           0
r10  -.18632124           0  -.33861797           0
r11  -.19646206   .01659561  -.34151619           0
r12  -.21131375   .04047685  -.34583088   .01657396

Algorithm is lasso

Cp, R-squared and Actions along the sequence of models

+----------------------------------------------+
| Step |      Cp     | R-square |  Action      |
|------+-------------+----------+--------------|
|    1 |    65.4654  |  0.0000  |              | 
|    2 |    61.4998  |  0.0266  | +meetroom    | 
|    3 |    53.1131  |  0.0728  | +electricity | 
|    4 |    43.4530  |  0.1248  | +count2009   | 
|    5 |    29.3709  |  0.1964  | +health      | 
|    6 |    11.5064  |  0.2849  | +recreation  | 
|    7 |    11.2395  |  0.2950  | +water       | 
|    8 |    10.7809  |  0.3060  | +sewer       | 
|    9 |    11.1000  |  0.3135  | +policesta   | 
|   10 |     8.7369 *|  0.3329  | +schoolbuild | 
|   11 |    10.2583  |  0.3350  | +temple      | 
|   12 |    12.0000  |  0.3362  | +road        | 
+----------------------------------------------+
* indicates the smallest value for Cp

The coefficient values for the minimum Cp

+----------------------------+
| Variable    |  Coefficient |
|-------------+--------------|
| count2009   |       0.0148 |
| schoolbuild |      -0.0095 |
| policesta   |       0.0254 |
| electricity |      -0.0727 |
| water       |      -0.0087 |
| sewer       |      -0.0181 |
| health      |      -0.0249 |
| recreation  |      -0.0323 |
| meetroom    |      -0.0577 |
+----------------------------+

. global ea_lasso="count2009 schoolbuild schoolbuild_miss policesta policesta_miss electricity e
> lectricity_miss water water_miss sewer sewer_miss health health_miss recreation recreation_mis
> s meetroom meetroom_miss"

. 
.         regress bsrenamo09 $treat $prov $ea_lasso if v==1 & time==1
note: schoolbuild omitted because of collinearity
note: schoolbuild_miss omitted because of collinearity
note: sewer_miss omitted because of collinearity

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 20,   140) =   14.57
       Model |  1.42750649    20  .071375324           Prob > F      =  0.0000
    Residual |  .685948136   140   .00489963           R-squared     =  0.6754
-------------+------------------------------           Adj R-squared =  0.6291
       Total |  2.11345462   160  .013209091           Root MSE      =     .07

----------------------------------------------------------------------------------
      bsrenamo09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       civiceduc |  -.0367917   .0158774    -2.32   0.022    -.0681821   -.0054013
         hotline |   -.007124   .0160945    -0.44   0.659    -.0389437    .0246957
         verdade |  -.0241104    .016538    -1.46   0.147    -.0568068     .008586
             pr1 |   .1094917   .0183876     5.95   0.000     .0731384    .1458449
             pr2 |  -.0796266   .0211505    -3.76   0.000    -.1214422    -.037811
             pr3 |  -.0989343    .018332    -5.40   0.000    -.1351776   -.0626909
       count2009 |   .0037572   .0025234     1.49   0.139    -.0012317     .008746
     schoolbuild |          0  (omitted)
schoolbuild_miss |          0  (omitted)
       policesta |   .0058207   .0156692     0.37   0.711    -.0251582    .0367997
  policesta_miss |   .0235003   .0757833     0.31   0.757    -.1263273    .1733279
     electricity |  -.0371374    .017324    -2.14   0.034    -.0713879    -.002887
electricity_miss |  -.0251639   .0755243    -0.33   0.739    -.1744795    .1241516
           water |   .0321501   .0190754     1.69   0.094     -.005563    .0698633
      water_miss |   .0436113   .1288139     0.34   0.735    -.2110606    .2982832
           sewer |   .0062844   .0211028     0.30   0.766     -.035437    .0480058
      sewer_miss |          0  (omitted)
          health |    -.01176   .0155458    -0.76   0.451     -.042495    .0189749
     health_miss |  -.0244758   .0512384    -0.48   0.634    -.1257768    .0768252
      recreation |  -.0235188   .0157282    -1.50   0.137    -.0546144    .0075768
 recreation_miss |   .0166109    .040755     0.41   0.684    -.0639639    .0971857
        meetroom |  -.0155649   .0150863    -1.03   0.304    -.0453912    .0142615
   meetroom_miss |  -.0047612   .0256732    -0.19   0.853    -.0555186    .0459961
           _cons |   .1710859   .0221502     7.72   0.000     .1272937     .214878
----------------------------------------------------------------------------------

.         estimates store l_11

.         sum bsrenamo09 if e(sample) & control == 1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  bsrenamo09 |        41    .1362756    .1364646          0   .5978793

.         scalar define m_l_11=r(mean)

.         display m_l_11
.13627558

.         test civiceduc = hotline

 ( 1)  civiceduc - hotline = 0

       F(  1,   140) =    3.39
            Prob > F =    0.0675

.         scalar define t1_l_11=r(p)

.         display t1_l_11
.06752101

.         test civiceduc = verdade

 ( 1)  civiceduc - verdade = 0

       F(  1,   140) =    0.62
            Prob > F =    0.4308

.         scalar define t2_l_11=r(p)

.         display t2_l_11
.43079342

.         test hotline = verdade

 ( 1)  hotline - verdade = 0

       F(  1,   140) =    1.04
            Prob > F =    0.3103

.         scalar define t3_l_11=r(p)

.         display t3_l_11
.31034725

.         test civiceduc hotline verdade

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   140) =    2.12
            Prob > F =    0.1002

.         scalar define t4_l_11=r(p)

.         display t4_l_11
.1002472

. 
. global list1=""

. global list2=""

. 
. global list1="$list1" + " l_1" + " l_2" + " l_3" + " l_4" + " l_5" + " l_6" + " l_7" + " l_8" 
> + " l_9" + " l_10" + " l_11"

. matrix define means=(m_l_1, m_l_2, m_l_3, m_l_4, m_l_5, m_l_6, m_l_7, m_l_8, m_l_9, m_l_10, m_
> l_11 \ t1_l_1, t1_l_2, t1_l_3, t1_l_4, t1_l_5, t1_l_6, t1_l_7, t1_l_8, t1_l_9, t1_l_10, t1_l_1
> 1 \ t2_l_1, t2_l_2, t2_l_3, t2_l_4, t2_l_5, t2_l_6, t2_l_7, t2_l_8, t2_l_9, t2_l_10, t2_l_11 \
>  t3_l_1, t3_l_2, t3_l_3, t3_l_4, t3_l_5, t3_l_6, t3_l_7, t3_l_8, t3_l_9, t3_l_10, t3_l_11 \ t4
> _l_1, t4_l_2, t4_l_3, t4_l_4, t4_l_5, t4_l_6, t4_l_7, t4_l_8, t4_l_9, t4_l_10, t4_l_11)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_ballots.xml") append sheet("lasso
> ") 


note: results saved to outputregs_ballots.xml

. xml_tab $list2, save("outputregs_ballots.xml") append sheet("lasso stats") 


note: results saved to outputregs_ballots.xml

. estimates clear

. 
. ************************************************
. *****  REGRESSIONS OF INDIVIDUAL BEHAVIOR  *****
. ************************************************
. 
. ********************************
. *****  INDIVIDUAL TURNOUT  *****
. ********************************
. 
. *tresp
. 
. * needs turnoutquest (original survey self-reported turnout question)
. capture drop tresp

. gen tresp=.
(3532 missing values generated)

. replace tresp=. if time==1 & (turnoutquest==1 | turnoutquest==2)
(0 real changes made)

. replace tresp=0 if time==1 & (turnoutquest==3 | turnoutquest==4)
(97 real changes made)

. replace tresp=1 if time==1 & turnoutquest==5
(1024 real changes made)

. 
. *intt
. 
. * needs turnoutquest, tintquestion (interviewer assessment question), dropselecquest (said did
>  not vote in one of the questions after self-reported turnout question)
. capture drop intt

. gen intt=.
(3532 missing values generated)

. replace intt=. if time==1 & (turnoutquest==1 | turnoutquest==2)
(0 real changes made)

. replace intt=0 if time==1 & (turnoutquest==3 | turnoutquest==4 | (turnoutquest==5 & tintquesti
> on==. & dropselecquest!=.))
(110 real changes made)

. replace intt=tintquestion if time==1 & turnoutquest==5 & tintquestion!=.
(1011 real changes made)

. replace intt=intt/7 if time==1
(1011 real changes made)

. 
. *tfinger
. 
. * needs turnoutquest, tfinger1 (showed inked finger question), dropselecquest
. capture drop tfinger

. gen tfinger=.
(3532 missing values generated)

. replace tfinger=. if time==1 & (turnoutquest==1 | turnoutquest==2)
(0 real changes made)

. replace tfinger=0 if time==1 & (turnoutquest==3 | turnoutquest==4 | (turnoutquest==5 & tfinger
> 1==. & dropselecquest!=.) | (turnoutquest==5 & tfinger1>1))
(170 real changes made)

. replace tfinger=1 if time==1 & turnoutquest==5 & tfinger1==1
(951 real changes made)

. 
. *tseen
. 
. * needs turnoutquest, tfinger2 (survey question on inked finger is inked or duration of inked 
> finger if not inked), dropselecquest
. capture drop tseen

. gen tseen=.
(3532 missing values generated)

. replace tseen=. if time==1 & (turnoutquest==1 | turnoutquest==2)
(0 real changes made)

. replace tseen=0 if time==1 & (turnoutquest==3 | turnoutquest==4 | (turnoutquest==5 & tfinger2=
> =. & dropselecquest!=.) | (turnoutquest==5 & tfinger2==999) | (turnoutquest==5 & tfinger2<998)
> )
(795 real changes made)

. replace tseen=1 if time==1 & turnoutquest==5 & tfinger2==998
(326 real changes made)

. 
. *dayselec
. 
. * needs dayselecpost
. capture drop pt_dayselec

. xtile pt_dayselec = dayselecpost if time==1, nq(100)

. 
. save mozdata_aux.dta, replace
(note: file mozdata_aux.dta not found)
file mozdata_aux.dta saved

. 
. **********************************************************************************************
> **
. *****  OA TABLE 8, OA TABLE 10 (PART), AND OA TABLE 11: REGRESSIONS OF INDIVIDUAL TURNOUT  ***
> **
. **********************************************************************************************
> **
. 
. global turnout1="tresp intt"

. global turnout2="tfinger tseen"

. 
. global ea="post post_miss health health_miss"

. global controls="sex age single divor protest com prof tea comform dom econfood house llomue c
> hitsua living"

. 
. global list1=""

. global list2=""

. 
. foreach i in $turnout1 {
  2. 
.         regress `i' $treat $prov if time==1, cluster(ea)
  3.         estimates store `i'_2_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2_1=r(mean)
  6.         display m_`i'_2_1
  7.         test civiceduc = hotline
  8.         scalar define t_`i'_2_1_1=r(p)
  9.         display t_`i'_2_1_1
 10.         test civiceduc = verdade
 11.         scalar define t_`i'_2_1_2=r(p)
 12.         display t_`i'_2_1_2
 13.         test hotline = verdade
 14.         scalar define t_`i'_2_1_3=r(p)
 15.         display t_`i'_2_1_3
 16.         test civiceduc hotline verdade
 17.         scalar define t_`i'_2_1_4=r(p)
 18.         display t_`i'_2_1_4
 19. 
.         regress `i' $treat $prov if time==1 & lazy==0, cluster(ea)
 20.         estimates store `i'_2_2
 21.         regress `i' $treat $prov if time==1 & lazy==0
 22.         estimates store `i'_2_2a
 23.         
.         regress `i' $treat $prov if time==1 & (lazy==1|control==1), cluster(ea)
 24.         estimates store `i'_2_3
 25.         regress `i' $treat $prov if time==1 & (lazy==1|control==1)
 26.         estimates store `i'_2_3a
 27. 
.         suest `i'_2_2a `i'_2_3a, cluster(ea)
 28.         test [`i'_2_2a_mean]civiceduc=[`i'_2_3a_mean]civiceduc  
 29.         scalar define t_`i'_2_5=r(p)
 30.         display t_`i'_2_5
 31.         test [`i'_2_2a_mean]hotline=[`i'_2_3a_mean]hotline      
 32.         scalar define t_`i'_2_6=r(p)
 33.         display t_`i'_2_6
 34.         test [`i'_2_2a_mean]verdade=[`i'_2_3a_mean]verdade
 35.         scalar define t_`i'_2_7=r(p)
 36.         display t_`i'_2_7
 37. 
.         regress `i' $treat $prov $ea $controls if time==1, cluster(ea)
 38.         estimates store `i'_3_1
 39.         sum `i' if e(sample) & control == 1
 40.         scalar define m_`i'_3_1=r(mean)
 41.         display m_`i'_3_1
 42.         test civiceduc = hotline
 43.         scalar define t_`i'_3_1_1=r(p)
 44.         display t_`i'_3_1_1
 45.         test civiceduc = verdade
 46.         scalar define t_`i'_3_1_2=r(p)
 47.         display t_`i'_3_1_2
 48.         test hotline = verdade
 49.         scalar define t_`i'_3_1_3=r(p)
 50.         display t_`i'_3_1_3
 51.         test civiceduc hotline verdade
 52.         scalar define t_`i'_3_1_4=r(p)
 53.         display t_`i'_3_1_4
 54. 
.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0, cluster(ea)
 55.         estimates store `i'_3_2
 56.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0
 57.         estimates store `i'_3_2a
 58.         
.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1), cluster(ea)
 59.         estimates store `i'_3_3
 60.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1)
 61.         estimates store `i'_3_3a
 62. 
.         suest `i'_3_2a `i'_3_3a, cluster(ea)
 63.         test [`i'_3_2a_mean]civiceduc=[`i'_3_3a_mean]civiceduc  
 64.         scalar define t_`i'_3_5=r(p)
 65.         display t_`i'_3_5
 66.         test [`i'_3_2a_mean]hotline=[`i'_3_3a_mean]hotline      
 67.         scalar define t_`i'_3_6=r(p)
 68.         display t_`i'_3_6
 69.         test [`i'_3_2a_mean]verdade=[`i'_3_3a_mean]verdade
 70.         scalar define t_`i'_3_7=r(p)
 71.         display t_`i'_3_7
 72.         
.         global list1="$list1" + " `i'_2_1" + " `i'_3_1" + " `i'_2_2" + " `i'_3_2"  + " `i'_2_3
> " + " `i'_3_3"
 73.         
.         }

Linear regression                                      Number of obs =    1121
                                                       F(  6,   160) =    2.93
                                                       Prob > F      =  0.0096
                                                       R-squared     =  0.0186
                                                       Root MSE      =   .2794

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       tresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0361871   .0260147     1.39   0.166    -.0151894    .0875636
     hotline |   .0720493    .023796     3.03   0.003     .0250545    .1190442
     verdade |   .0304764   .0297133     1.03   0.307    -.0282044    .0891572
         pr1 |  -.0601966   .0287547    -2.09   0.038    -.1169843   -.0034089
         pr2 |  -.0278695   .0233722    -1.19   0.235    -.0740272    .0182882
         pr3 |   .0122373   .0203527     0.60   0.549    -.0279573    .0524319
       _cons |   .8971342   .0243318    36.87   0.000     .8490813     .945187
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tresp |       269    .8773234    .3286771          0          1
.87732342

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    3.11
            Prob > F =    0.0796
.07958484

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.05
            Prob > F =    0.8320
.83202655

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    2.82
            Prob > F =    0.0948
.09481286

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    3.50
            Prob > F =    0.0170
.01697172

Linear regression                                      Number of obs =     953
                                                       F(  6,   160) =    2.36
                                                       Prob > F      =  0.0326
                                                       R-squared     =  0.0173
                                                       Root MSE      =  .28805

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       tresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0384781   .0268208     1.43   0.153    -.0144904    .0914466
     hotline |   .0641835    .025311     2.54   0.012     .0141967    .1141703
     verdade |   .0205797    .029769     0.69   0.490    -.0382112    .0793706
         pr1 |  -.0640235    .029531    -2.17   0.032    -.1223442   -.0057028
         pr2 |  -.0390228   .0253128    -1.54   0.125    -.0890132    .0109675
         pr3 |   .0069105   .0227032     0.30   0.761     -.037926     .051747
       _cons |   .9022979   .0247431    36.47   0.000     .8534328     .951163
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     953
-------------+------------------------------           F(  6,   946) =    2.78
       Model |  1.38176586     6   .23029431           Prob > F      =  0.0111
    Residual |   78.492316   946   .08297285           R-squared     =  0.0173
-------------+------------------------------           Adj R-squared =  0.0111
       Total |  79.8740818   952  .083901346           Root MSE      =  .28805

------------------------------------------------------------------------------
       tresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0384781   .0255787     1.50   0.133    -.0117195    .0886757
     hotline |   .0641835   .0258835     2.48   0.013     .0133877    .1149793
     verdade |   .0205797   .0263929     0.78   0.436    -.0312157    .0723751
         pr1 |  -.0640235   .0263653    -2.43   0.015    -.1157647   -.0122823
         pr2 |  -.0390228   .0262448    -1.49   0.137    -.0905276    .0124819
         pr3 |   .0069105   .0266139     0.26   0.795    -.0453186    .0591396
       _cons |   .9022979    .024051    37.52   0.000     .8550984    .9494974
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     437
                                                       F(  6,   152) =    3.40
                                                       Prob > F      =  0.0035
                                                       R-squared     =  0.0268
                                                       Root MSE      =  .29312

                                   (Std. Err. adjusted for 153 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       tresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0240299   .0434366     0.55   0.581    -.0617876    .1098474
     hotline |   .1023962   .0260405     3.93   0.000      .050948    .1538443
     verdade |    .070331   .0363052     1.94   0.055     -.001397     .142059
         pr1 |  -.0327486   .0436665    -0.75   0.454    -.1190201     .053523
         pr2 |  -.0435958   .0373379    -1.17   0.245    -.1173641    .0301726
         pr3 |   .0297775   .0323603     0.92   0.359    -.0341565    .0937116
       _cons |   .8897219   .0291035    30.57   0.000     .8322224    .9472214
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     437
-------------+------------------------------           F(  6,   430) =    1.98
       Model |  1.01893539     6  .169822566           Prob > F      =  0.0677
    Residual |  36.9444513   430  .085917329           R-squared     =  0.0268
-------------+------------------------------           Adj R-squared =  0.0133
       Total |  37.9633867   436  .087071988           Root MSE      =  .29312

------------------------------------------------------------------------------
       tresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0240299    .044104     0.54   0.586    -.0626564    .1107162
     hotline |   .1023962   .0422117     2.43   0.016     .0194292    .1853631
     verdade |    .070331   .0431419     1.63   0.104    -.0144643    .1551263
         pr1 |  -.0327486   .0395118    -0.83   0.408    -.1104088    .0449116
         pr2 |  -.0435958   .0395987    -1.10   0.272    -.1214268    .0342353
         pr3 |   .0297775   .0396153     0.75   0.453    -.0480863    .1076413
       _cons |   .8897219   .0303503    29.32   0.000     .8300685    .9493753
------------------------------------------------------------------------------

Simultaneous results for tresp_2_2a, tresp_2_3a

                                                  Number of obs   =       1121

                                       (Std. Err. adjusted for 161 clusters in ea)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
tresp_2_2a_mean  |
       civiceduc |   .0384781   .0267362     1.44   0.150    -.0139238    .0908801
         hotline |   .0641835   .0252312     2.54   0.011     .0147313    .1136357
         verdade |   .0205797   .0296751     0.69   0.488    -.0375824    .0787418
             pr1 |  -.0640235   .0294377    -2.17   0.030    -.1217204   -.0063266
             pr2 |  -.0390228   .0252329    -1.55   0.122    -.0884785    .0104328
             pr3 |   .0069105   .0226315     0.31   0.760    -.0374465    .0512674
           _cons |   .9022979    .024665    36.58   0.000     .8539554    .9506403
-----------------+----------------------------------------------------------------
tresp_2_2a_lnvar |
           _cons |  -2.489242    .094563   -26.32   0.000    -2.674582   -2.303902
-----------------+----------------------------------------------------------------
tresp_2_3a_mean  |
       civiceduc |   .0240299   .0431297     0.56   0.577    -.0605027    .1085625
         hotline |   .1023962   .0258565     3.96   0.000     .0517183     .153074
         verdade |    .070331   .0360487     1.95   0.051    -.0003231    .1409851
             pr1 |  -.0327486   .0433579    -0.76   0.450    -.1177285    .0522313
             pr2 |  -.0435958   .0370741    -1.18   0.240    -.1162596    .0290681
             pr3 |   .0297775   .0321316     0.93   0.354    -.0331993    .0927543
           _cons |   .8897219   .0288978    30.79   0.000     .8330833    .9463605
-----------------+----------------------------------------------------------------
tresp_2_3a_lnvar |
           _cons |   -2.45437   .1294922   -18.95   0.000     -2.70817    -2.20057
----------------------------------------------------------------------------------

 ( 1)  [tresp_2_2a_mean]civiceduc - [tresp_2_3a_mean]civiceduc = 0

           chi2(  1) =    0.12
         Prob > chi2 =    0.7301
.73005172

 ( 1)  [tresp_2_2a_mean]hotline - [tresp_2_3a_mean]hotline = 0

           chi2(  1) =    2.57
         Prob > chi2 =    0.1092
.10916726

 ( 1)  [tresp_2_2a_mean]verdade - [tresp_2_3a_mean]verdade = 0

           chi2(  1) =    4.29
         Prob > chi2 =    0.0384
.0383881

Linear regression                                      Number of obs =    1106
                                                       F( 25,   160) =    2.30
                                                       Prob > F      =  0.0010
                                                       R-squared     =  0.0595
                                                       Root MSE      =    .275

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       tresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0364822    .025729     1.42   0.158    -.0143301    .0872945
     hotline |   .0645514   .0235992     2.74   0.007     .0179453    .1111576
     verdade |   .0323676    .027271     1.19   0.237    -.0214899    .0862251
         pr1 |  -.0362041   .0309706    -1.17   0.244     -.097368    .0249598
         pr2 |  -.0050979   .0288958    -0.18   0.860    -.0621643    .0519685
         pr3 |   .0003235   .0252073     0.01   0.990    -.0494584    .0501054
        post |  -.0457668    .030925    -1.48   0.141    -.1068407    .0153072
   post_miss |   .0038929   .0363603     0.11   0.915    -.0679152    .0757009
      health |   .0056941   .0176733     0.32   0.748    -.0292089    .0405971
 health_miss |  -.0168947   .0459843    -0.37   0.714    -.1077091    .0739198
         sex |   .0440705   .0174374     2.53   0.012     .0096334    .0785076
         age |  -.0014263   .0007825    -1.82   0.070    -.0029716    .0001191
      single |  -.0559429   .0279522    -2.00   0.047    -.1111457   -.0007401
       divor |  -.0014223   .1131505    -0.01   0.990    -.2248833    .2220388
     protest |   .0016919   .0217835     0.08   0.938    -.0413284    .0447123
         com |  -.0413266   .0418427    -0.99   0.325    -.1239619    .0413087
        prof |   .0680568   .0205127     3.32   0.001     .0275462    .1085674
         tea |  -.0251781   .0452187    -0.56   0.578    -.1144805    .0641243
     comform |  -.2034734   .1090281    -1.87   0.064    -.4187932    .0118463
         dom |  -.0563849   .0339372    -1.66   0.099    -.1234075    .0106376
    econfood |  -.0138196   .0075737    -1.82   0.070    -.0287769    .0011378
       house |  -.0017411     .02594    -0.07   0.947    -.0529701    .0494879
      llomue |  -.0373097   .0427005    -0.87   0.384     -.121639    .0470195
     chitsua |  -.0535348   .0783651    -0.68   0.496    -.2082983    .1012286
      living |   .0265831    .011044     2.41   0.017     .0047723    .0483939
       _cons |   .8824566   .0495226    17.82   0.000     .7846543    .9802588
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tresp |       267    .8764045    .3297376          0          1
.87640449

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    1.97
            Prob > F =    0.1622
.16221792

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.03
            Prob > F =    0.8715
.87145818

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    1.97
            Prob > F =    0.1620
.16199876

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    2.68
            Prob > F =    0.0491
.04907816

Linear regression                                      Number of obs =     943
                                                       F( 25,   160) =    3.46
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0561
                                                       Root MSE      =  .28364

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       tresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0337731   .0270682     1.25   0.214    -.0196838    .0872301
     hotline |   .0577012   .0256391     2.25   0.026     .0070664    .1083359
     verdade |   .0199809   .0277303     0.72   0.472    -.0347838    .0747455
         pr1 |  -.0465295   .0322305    -1.44   0.151    -.1101816    .0171227
         pr2 |   -.020513    .030976    -0.66   0.509    -.0816876    .0406615
         pr3 |  -.0083192   .0285596    -0.29   0.771    -.0647216    .0480832
        post |  -.0587263   .0342262    -1.72   0.088    -.1263197     .008867
   post_miss |    .000757   .0395094     0.02   0.985    -.0772701    .0787842
      health |   .0140372   .0192946     0.73   0.468    -.0240676    .0521421
 health_miss |  -.0221464    .050892    -0.44   0.664    -.1226532    .0783603
         sex |   .0510782   .0203538     2.51   0.013     .0108814    .0912749
         age |  -.0016696   .0008609    -1.94   0.054    -.0033698    .0000306
      single |   -.071281   .0319285    -2.23   0.027    -.1343367   -.0082253
       divor |   .1387536   .0222221     6.24   0.000     .0948672      .18264
     protest |   .0038712   .0243626     0.16   0.874    -.0442426     .051985
         com |  -.0422871   .0452673    -0.93   0.352    -.1316856    .0471114
        prof |   .0902136   .0216995     4.16   0.000     .0473591     .133068
         tea |   -.036365   .0533765    -0.68   0.497    -.1417784    .0690485
     comform |  -.0931712   .1018618    -0.91   0.362    -.2943382    .1079958
         dom |  -.0456885   .0346417    -1.32   0.189    -.1141023    .0227254
    econfood |  -.0123866   .0085458    -1.45   0.149    -.0292638    .0044906
       house |   .0007457   .0299109     0.02   0.980    -.0583254    .0598168
      llomue |  -.0293647   .0437481    -0.67   0.503    -.1157629    .0570336
     chitsua |  -.0706387   .0973997    -0.73   0.469    -.2629935    .1217161
      living |   .0248995   .0112074     2.22   0.028      .002766     .047033
       _cons |   .8942806   .0519869    17.20   0.000     .7916116    .9969496
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     943
-------------+------------------------------           F( 25,   917) =    2.18
       Model |  4.38216367    25  .175286547           Prob > F      =  0.0008
    Residual |  73.7747822   917  .080452325           R-squared     =  0.0561
-------------+------------------------------           Adj R-squared =  0.0303
       Total |  78.1569459   942  .082969157           Root MSE      =  .28364

------------------------------------------------------------------------------
       tresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0337731   .0265807     1.27   0.204    -.0183929    .0859392
     hotline |   .0577012   .0264236     2.18   0.029     .0058434     .109559
     verdade |   .0199809   .0271325     0.74   0.462    -.0332681    .0732298
         pr1 |  -.0465295   .0321973    -1.45   0.149    -.1097184    .0166594
         pr2 |   -.020513   .0309557    -0.66   0.508    -.0812652    .0402391
         pr3 |  -.0083192   .0301983    -0.28   0.783     -.067585    .0509466
        post |  -.0587263    .031807    -1.85   0.065    -.1211492    .0036965
   post_miss |    .000757    .053644     0.01   0.989    -.1045222    .1060363
      health |   .0140372   .0222185     0.63   0.528    -.0295678    .0576423
 health_miss |  -.0221464   .0589874    -0.38   0.707    -.1379125    .0936196
         sex |   .0510782   .0199665     2.56   0.011     .0118929    .0902635
         age |  -.0016696   .0007753    -2.15   0.032    -.0031912    -.000148
      single |   -.071281   .0257477    -2.77   0.006    -.1218123   -.0207497
       divor |   .1387536   .1094266     1.27   0.205     -.076002    .3535091
     protest |   .0038712     .02264     0.17   0.864     -.040561    .0483034
         com |  -.0422871   .0442544    -0.96   0.340    -.1291388    .0445646
        prof |   .0902136    .074988     1.20   0.229    -.0569545    .2373816
         tea |   -.036365   .0456575    -0.80   0.426    -.1259704    .0532405
     comform |  -.0931712   .0875894    -1.06   0.288    -.2650701    .0787278
         dom |  -.0456885   .0286741    -1.59   0.111    -.1019629     .010586
    econfood |  -.0123866   .0082471    -1.50   0.133     -.028572    .0037988
       house |   .0007457   .0271144     0.03   0.978    -.0524677    .0539591
      llomue |  -.0293647   .0376756    -0.78   0.436     -.103305    .0445757
     chitsua |  -.0706387   .0845694    -0.84   0.404    -.2366107    .0953333
      living |   .0248995    .009373     2.66   0.008     .0065046    .0432944
       _cons |   .8942806   .0511967    17.47   0.000     .7938043     .994757
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     430
                                                       F( 25,   151) =    3.19
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1324
                                                       Root MSE      =  .28528

                                   (Std. Err. adjusted for 152 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       tresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0484097   .0455907     1.06   0.290    -.0416685    .1384878
     hotline |   .0956975   .0249333     3.84   0.000     .0464344    .1449607
     verdade |   .0947805   .0321443     2.95   0.004     .0312699    .1582911
         pr1 |   .0040893   .0543961     0.08   0.940    -.1033864    .1115651
         pr2 |   .0105164   .0486471     0.22   0.829    -.0856004    .1066333
         pr3 |   .0210393   .0389802     0.54   0.590    -.0559778    .0980564
        post |  -.0224621   .0394834    -0.57   0.570    -.1004733    .0555491
   post_miss |   .0043769   .0368539     0.12   0.906     -.068439    .0771929
      health |   .0030897   .0287669     0.11   0.915    -.0537479    .0599273
 health_miss |   .0763279   .0378131     2.02   0.045     .0016168     .151039
         sex |   .0363162   .0280777     1.29   0.198    -.0191597    .0917921
         age |  -.0027716   .0015541    -1.78   0.077    -.0058423     .000299
      single |   -.085465   .0464747    -1.84   0.068    -.1772897    .0063597
       divor |  -.2744923    .279309    -0.98   0.327    -.8263508    .2773661
     protest |  -.0178199   .0370836    -0.48   0.632    -.0910896    .0554499
         com |   -.000615    .070403    -0.01   0.993    -.1397172    .1384873
        prof |   .0680202   .0366646     1.86   0.066    -.0044218    .1404621
         tea |  -.0340344   .0661721    -0.51   0.608    -.1647773    .0967084
     comform |  -.5474958   .2144869    -2.55   0.012    -.9712787   -.1237129
         dom |  -.0736329   .0511996    -1.44   0.152     -.174793    .0275272
    econfood |  -.0128032   .0131186    -0.98   0.331    -.0387228    .0131165
       house |   .0081247   .0518481     0.16   0.876    -.0943167    .1105661
      llomue |  -.0440586   .0591992    -0.74   0.458    -.1610243    .0729071
     chitsua |  -.0985139    .125468    -0.79   0.434    -.3464134    .1493857
      living |   .0363301   .0180653     2.01   0.046     .0006367    .0720234
       _cons |   .8888659   .0868337    10.24   0.000     .7172999    1.060432
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     430
-------------+------------------------------           F( 25,   404) =    2.47
       Model |  5.01716983    25  .200686793           Prob > F      =  0.0001
    Residual |  32.8805046   404  .081387388           R-squared     =  0.1324
-------------+------------------------------           Adj R-squared =  0.0787
       Total |  37.8976744   429  .088339567           Root MSE      =  .28528

------------------------------------------------------------------------------
       tresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0484097   .0454486     1.07   0.287    -.0409355    .1377549
     hotline |   .0956975   .0433019     2.21   0.028     .0105725    .1808226
     verdade |   .0947805   .0446646     2.12   0.034     .0069765    .1825845
         pr1 |   .0040893   .0494209     0.08   0.934    -.0930649    .1012435
         pr2 |   .0105164   .0464859     0.23   0.821    -.0808679    .1019008
         pr3 |   .0210393   .0461135     0.46   0.648    -.0696131    .1116916
        post |  -.0224621   .0408794    -0.55   0.583    -.1028251    .0579008
   post_miss |   .0043769   .0695127     0.06   0.950    -.1322748    .1410286
      health |   .0030897   .0351754     0.09   0.930      -.06606    .0722394
 health_miss |   .0763279   .1025281     0.74   0.457    -.1252273    .2778831
         sex |   .0363162   .0300018     1.21   0.227    -.0226629    .0952954
         age |  -.0027716   .0012247    -2.26   0.024    -.0051792   -.0003641
      single |   -.085465   .0367198    -2.33   0.020    -.1576507   -.0132793
       divor |  -.2744923   .1680488    -1.63   0.103    -.6048517     .055867
     protest |  -.0178199   .0352826    -0.51   0.614    -.0871804    .0515406
         com |   -.000615   .0768298    -0.01   0.994    -.1516511    .1504212
        prof |   .0680202   .0986318     0.69   0.491    -.1258755    .2619159
         tea |  -.0340344   .0602791    -0.56   0.573    -.1525344    .0844655
     comform |  -.5474958   .1200161    -4.56   0.000    -.7834299   -.3115618
         dom |  -.0736329   .0424665    -1.73   0.084    -.1571159    .0098501
    econfood |  -.0128032   .0128573    -1.00   0.320    -.0380788    .0124724
       house |   .0081247   .0401616     0.20   0.840    -.0708272    .0870765
      llomue |  -.0440586   .0577117    -0.76   0.446    -.1575114    .0693942
     chitsua |  -.0985139   .1332476    -0.74   0.460    -.3604591    .1634314
      living |   .0363301   .0135662     2.68   0.008     .0096609    .0629992
       _cons |   .8888659   .0760037    11.70   0.000     .7394538    1.038278
------------------------------------------------------------------------------

Simultaneous results for tresp_3_2a, tresp_3_3a

                                                  Number of obs   =       1106

                                       (Std. Err. adjusted for 161 clusters in ea)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
tresp_3_2a_mean  |
       civiceduc |   .0337731   .0267066     1.26   0.206    -.0185707     .086117
         hotline |   .0577012   .0252966     2.28   0.023     .0081207    .1072816
         verdade |   .0199809   .0273599     0.73   0.465    -.0336435    .0736052
             pr1 |  -.0465295      .0318    -1.46   0.143    -.1088563    .0157973
             pr2 |   -.020513   .0305622    -0.67   0.502    -.0804138    .0393878
             pr3 |  -.0083192   .0281781    -0.30   0.768    -.0635472    .0469088
            post |  -.0587263    .033769    -1.74   0.082    -.1249123    .0074596
       post_miss |    .000757   .0389816     0.02   0.985    -.0756454    .0771595
          health |   .0140372   .0190368     0.74   0.461    -.0232742    .0513487
     health_miss |  -.0221464   .0502122    -0.44   0.659    -.1205605    .0762676
             sex |   .0510782   .0200819     2.54   0.011     .0117184     .090438
             age |  -.0016696   .0008494    -1.97   0.049    -.0033344   -4.80e-06
          single |   -.071281    .031502    -2.26   0.024    -.1330238   -.0095382
           divor |   .1387536   .0219252     6.33   0.000     .0957809    .1817262
         protest |   .0038712   .0240372     0.16   0.872    -.0432408    .0509832
             com |  -.0422871   .0446626    -0.95   0.344    -.1298242      .04525
            prof |   .0902136   .0214096     4.21   0.000     .0482514    .1321757
             tea |   -.036365   .0526635    -0.69   0.490    -.1395835    .0668536
         comform |  -.0931712    .100501    -0.93   0.354    -.2901495    .1038072
             dom |  -.0456885   .0341789    -1.34   0.181    -.1126779    .0213009
        econfood |  -.0123866   .0084317    -1.47   0.142    -.0289124    .0041392
           house |   .0007457   .0295113     0.03   0.980    -.0570955    .0585869
          llomue |  -.0293647   .0431637    -0.68   0.496     -.113964    .0552346
         chitsua |  -.0706387   .0960985    -0.74   0.462    -.2589884     .117711
          living |   .0248995   .0110577     2.25   0.024     .0032268    .0465722
           _cons |   .8942806   .0512924    17.43   0.000     .7937494    .9948119
-----------------+----------------------------------------------------------------
tresp_3_2a_lnvar |
           _cons |  -2.520091   .0906221   -27.81   0.000    -2.697707   -2.342475
-----------------+----------------------------------------------------------------
tresp_3_3a_mean  |
       civiceduc |   .0484097   .0442342     1.09   0.274    -.0382878    .1351072
         hotline |   .0956975   .0241914     3.96   0.000     .0482833    .1431118
         verdade |   .0947805   .0311878     3.04   0.002     .0336535    .1559075
             pr1 |   .0040893   .0527776     0.08   0.938    -.0993528    .1075315
             pr2 |   .0105164   .0471996     0.22   0.824    -.0819931     .103026
             pr3 |   .0210393   .0378204     0.56   0.578    -.0530873    .0951659
            post |  -.0224621   .0383086    -0.59   0.558    -.0975455    .0526213
       post_miss |   .0043769   .0357574     0.12   0.903    -.0657062    .0744601
          health |   .0030897   .0279109     0.11   0.912    -.0516147    .0577941
     health_miss |   .0763279    .036688     2.08   0.037     .0044207    .1482351
             sex |   .0363162   .0272423     1.33   0.183    -.0170777    .0897101
             age |  -.0027716   .0015079    -1.84   0.066     -.005727    .0001837
          single |   -.085465   .0450919    -1.90   0.058    -.1738435    .0029135
           divor |  -.2744923   .2709983    -1.01   0.311    -.8056393    .2566547
         protest |  -.0178199   .0359802    -0.50   0.620    -.0883398       .0527
             com |   -.000615   .0683082    -0.01   0.993    -.1344966    .1332667
            prof |   .0680202   .0355737     1.91   0.056     -.001703    .1377433
             tea |  -.0340344   .0642032    -0.53   0.596    -.1598705    .0918016
         comform |  -.5474958   .2081049    -2.63   0.009     -.955374   -.1396177
             dom |  -.0736329   .0496762    -1.48   0.138    -.1709964    .0237306
        econfood |  -.0128032   .0127282    -1.01   0.314    -.0377501    .0121437
           house |   .0081247   .0503054     0.16   0.872     -.090472    .1067214
          llomue |  -.0440586   .0574377    -0.77   0.443    -.1566345    .0685173
         chitsua |  -.0985139   .1217348    -0.81   0.418    -.3371096    .1400819
          living |   .0363301   .0175278     2.07   0.038     .0019763    .0706839
           _cons |   .8888659     .08425    10.55   0.000     .7237389    1.053993
-----------------+----------------------------------------------------------------
tresp_3_3a_lnvar |
           _cons |  -2.508535   .1196286   -20.97   0.000    -2.743003   -2.274067
----------------------------------------------------------------------------------

 ( 1)  [tresp_3_2a_mean]civiceduc - [tresp_3_3a_mean]civiceduc = 0

           chi2(  1) =    0.12
         Prob > chi2 =    0.7328
.73279958

 ( 1)  [tresp_3_2a_mean]hotline - [tresp_3_3a_mean]hotline = 0

           chi2(  1) =    2.75
         Prob > chi2 =    0.0974
.09736075

 ( 1)  [tresp_3_2a_mean]verdade - [tresp_3_3a_mean]verdade = 0

           chi2(  1) =    8.80
         Prob > chi2 =    0.0030
.00300879

Linear regression                                      Number of obs =    1121
                                                       F(  6,   160) =    8.86
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0350
                                                       Root MSE      =  .31294

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        intt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0660298   .0257299     2.57   0.011     .0152158    .1168439
     hotline |   .0936726   .0243587     3.85   0.000     .0455666    .1417785
     verdade |   .0589786   .0344119     1.71   0.088    -.0089815    .1269386
         pr1 |  -.0025196   .0327039    -0.08   0.939    -.0671066    .0620673
         pr2 |   .0828998   .0248115     3.34   0.001     .0338995    .1319001
         pr3 |   .1071481   .0218279     4.91   0.000     .0640401     .150256
       _cons |   .7072181   .0249681    28.32   0.000     .6579086    .7565275
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
        intt |       269    .7541158    .3534014          0          1
.75411578

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    1.65
            Prob > F =    0.2007
.20068094

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.05
            Prob > F =    0.8278
.82783612

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    1.25
            Prob > F =    0.2646
.26457843

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    4.96
            Prob > F =    0.0026
.00256861

Linear regression                                      Number of obs =     953
                                                       F(  6,   160) =    6.35
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0320
                                                       Root MSE      =  .32025

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        intt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0719744   .0276547     2.60   0.010     .0173592    .1265897
     hotline |   .0919019   .0249651     3.68   0.000     .0425983    .1412054
     verdade |   .0365533   .0363366     1.01   0.316     -.035208    .1083145
         pr1 |   .0012411   .0340541     0.04   0.971    -.0660123    .0684945
         pr2 |   .0750474   .0273671     2.74   0.007         .021    .1290947
         pr3 |   .1031059   .0249084     4.14   0.000     .0539143    .1522976
       _cons |   .7092454   .0257286    27.57   0.000     .6584339    .7600568
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     953
-------------+------------------------------           F(  6,   946) =    5.22
       Model |  3.21025487     6  .535042478           Prob > F      =  0.0000
    Residual |  97.0231643   946  .102561484           R-squared     =  0.0320
-------------+------------------------------           Adj R-squared =  0.0259
       Total |  100.233419   952  .105287205           Root MSE      =  .32025

------------------------------------------------------------------------------
        intt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0719744   .0284383     2.53   0.012     .0161651    .1277838
     hotline |   .0919019   .0287772     3.19   0.001     .0354275    .1483763
     verdade |   .0365533   .0293434     1.25   0.213    -.0210325     .094139
         pr1 |   .0012411   .0293128     0.04   0.966    -.0562844    .0587667
         pr2 |   .0750474   .0291788     2.57   0.010     .0177848      .13231
         pr3 |   .1031059   .0295892     3.48   0.001      .045038    .1611739
       _cons |   .7092454   .0267398    26.52   0.000     .6567693    .7617215
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     437
                                                       F(  6,   152) =    5.10
                                                       Prob > F      =  0.0001
                                                       R-squared     =  0.0416
                                                       Root MSE      =  .32337

                                   (Std. Err. adjusted for 153 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        intt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0379312   .0434389     0.87   0.384    -.0478909    .1237532
     hotline |   .1001379   .0378497     2.65   0.009     .0253586    .1749173
     verdade |   .1448146   .0374784     3.86   0.000     .0707686    .2188605
         pr1 |   .0024785    .043284     0.06   0.954    -.0830375    .0879944
         pr2 |    .052613   .0401032     1.31   0.192    -.0266185    .1318446
         pr3 |    .101076    .036074     2.80   0.006     .0298049    .1723472
       _cons |    .715176   .0307167    23.28   0.000     .6544893    .7758627
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     437
-------------+------------------------------           F(  6,   430) =    3.11
       Model |  1.94938151     6  .324896919           Prob > F      =  0.0054
    Residual |  44.9642688   430  .104568067           R-squared     =  0.0416
-------------+------------------------------           Adj R-squared =  0.0282
       Total |  46.9136504   436  .107600115           Root MSE      =  .32337

------------------------------------------------------------------------------
        intt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0379312   .0486561     0.78   0.436    -.0577023    .1335646
     hotline |   .1001379   .0465685     2.15   0.032     .0086077    .1916681
     verdade |   .1448146   .0475947     3.04   0.002     .0512673    .2383618
         pr1 |   .0024785   .0435899     0.06   0.955    -.0831972    .0881542
         pr2 |    .052613   .0436858     1.20   0.229    -.0332512    .1384772
         pr3 |    .101076   .0437041     2.31   0.021     .0151757    .1869764
       _cons |    .715176   .0334829    21.36   0.000     .6493655    .7809864
------------------------------------------------------------------------------

Simultaneous results for intt_2_2a, intt_2_3a

                                                  Number of obs   =       1121

                                      (Std. Err. adjusted for 161 clusters in ea)
---------------------------------------------------------------------------------
                |               Robust
                |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
intt_2_2a_mean  |
      civiceduc |   .0719744   .0275674     2.61   0.009     .0179433    .1260055
        hotline |   .0919019   .0248863     3.69   0.000     .0431257    .1406781
        verdade |   .0365533   .0362219     1.01   0.313    -.0344404     .107547
            pr1 |   .0012411   .0339466     0.04   0.971    -.0652929    .0677752
            pr2 |   .0750474   .0272807     2.75   0.006     .0215781    .1285166
            pr3 |   .1031059   .0248298     4.15   0.000     .0544405    .1517714
          _cons |   .7092454   .0256474    27.65   0.000     .6589774    .7595133
----------------+----------------------------------------------------------------
intt_2_2a_lnvar |
          _cons |  -2.277293   .0647826   -35.15   0.000    -2.404264   -2.150321
----------------+----------------------------------------------------------------
intt_2_3a_mean  |
      civiceduc |   .0379312    .043132     0.88   0.379    -.0466059    .1224682
        hotline |   .1001379   .0375822     2.66   0.008     .0264782    .1737976
        verdade |   .1448146   .0372136     3.89   0.000     .0718773    .2177519
            pr1 |   .0024785   .0429781     0.06   0.954    -.0817571    .0867141
            pr2 |    .052613   .0398198     1.32   0.186    -.0254323    .1306583
            pr3 |    .101076   .0358191     2.82   0.005      .030872    .1712801
          _cons |    .715176   .0304996    23.45   0.000     .6553979     .774954
----------------+----------------------------------------------------------------
intt_2_3a_lnvar |
          _cons |  -2.257917    .081456   -27.72   0.000    -2.417568   -2.098266
---------------------------------------------------------------------------------

 ( 1)  [intt_2_2a_mean]civiceduc - [intt_2_3a_mean]civiceduc = 0

           chi2(  1) =    0.54
         Prob > chi2 =    0.4612
.46119623

 ( 1)  [intt_2_2a_mean]hotline - [intt_2_3a_mean]hotline = 0

           chi2(  1) =    0.05
         Prob > chi2 =    0.8168
.81678038

 ( 1)  [intt_2_2a_mean]verdade - [intt_2_3a_mean]verdade = 0

           chi2(  1) =   12.41
         Prob > chi2 =    0.0004
.00042641

Linear regression                                      Number of obs =    1106
                                                       F( 25,   160) =    5.94
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0774
                                                       Root MSE      =  .30857

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        intt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0743972   .0245324     3.03   0.003     .0259481    .1228463
     hotline |   .0873971   .0232319     3.76   0.000     .0415162    .1332779
     verdade |   .0663262   .0290924     2.28   0.024     .0088717    .1237808
         pr1 |   .0552852   .0299328     1.85   0.067    -.0038291    .1143996
         pr2 |   .0851542   .0314159     2.71   0.007     .0231108    .1471976
         pr3 |   .0886327   .0286998     3.09   0.002     .0319534     .145312
        post |  -.0156832   .0320036    -0.49   0.625    -.0788873    .0475208
   post_miss |  -.0013097   .0598267    -0.02   0.983    -.1194615    .1168421
      health |   .0124309   .0193486     0.64   0.521    -.0257806    .0506425
 health_miss |   .0060829    .053434     0.11   0.910    -.0994439    .1116097
         sex |   .0448856   .0197021     2.28   0.024      .005976    .0837953
         age |  -.0004145   .0008367    -0.50   0.621     -.002067     .001238
      single |  -.0229568   .0292251    -0.79   0.433    -.0806734    .0347599
       divor |   .0344724   .0982186     0.35   0.726    -.1594997    .2284444
     protest |   .0186018   .0229941     0.81   0.420    -.0268092    .0640129
         com |  -.0932479   .0469787    -1.98   0.049    -.1860262   -.0004697
        prof |   .1671856    .025433     6.57   0.000      .116958    .2174133
         tea |     .05486    .044381     1.24   0.218     -.032788    .1425081
     comform |  -.1560514   .1007513    -1.55   0.123    -.3550253    .0429224
         dom |   .0003951   .0350909     0.01   0.991     -.068906    .0696962
    econfood |   -.014562   .0091369    -1.59   0.113    -.0326065    .0034826
       house |   .0267832   .0284616     0.94   0.348    -.0294255     .082992
      llomue |  -.1032393   .0495931    -2.08   0.039    -.2011808   -.0052977
     chitsua |  -.0428565   .0711214    -0.60   0.548    -.1833143    .0976012
      living |   .0347342   .0105606     3.29   0.001     .0138779    .0555904
       _cons |   .5752975   .0495218    11.62   0.000     .4774969    .6730981
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
        intt |       267     .753344    .3546142          0          1
.75334404

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.35
            Prob > F =    0.5569
.5568917

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.08
            Prob > F =    0.7838
.78375084

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.63
            Prob > F =    0.4293
.42926394

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    5.12
            Prob > F =    0.0021
.00207552

Linear regression                                      Number of obs =     943
                                                       F( 25,   160) =    5.73
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0713
                                                       Root MSE      =  .31639

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        intt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0753457   .0267348     2.82   0.005     .0225471    .1281443
     hotline |   .0873785   .0241676     3.62   0.000     .0396498    .1351071
     verdade |   .0423494    .031286     1.35   0.178    -.0194374    .1041362
         pr1 |   .0556034   .0304393     1.83   0.070    -.0045112     .115718
         pr2 |    .072729   .0338006     2.15   0.033      .005976    .1394819
         pr3 |   .0849818   .0319394     2.66   0.009     .0219046    .1480589
        post |  -.0182908   .0341235    -0.54   0.593    -.0856813    .0490996
   post_miss |   -.015867    .060511    -0.26   0.793    -.1353703    .1036363
      health |    .012081   .0218325     0.55   0.581    -.0310361    .0551981
 health_miss |  -.0027855   .0572093    -0.05   0.961    -.1157683    .1101973
         sex |   .0504879   .0223755     2.26   0.025     .0062986    .0946773
         age |  -.0006034   .0009001    -0.67   0.504     -.002381    .0011743
      single |  -.0361656   .0339727    -1.06   0.289    -.1032584    .0309273
       divor |   .1596039   .0368022     4.34   0.000     .0869232    .2322846
     protest |    .021036   .0249008     0.84   0.399    -.0281405    .0702126
         com |  -.1058584    .051549    -2.05   0.042    -.2076625   -.0040543
        prof |   .1813596   .0268826     6.75   0.000     .1282691      .23445
         tea |   .0354388   .0509678     0.70   0.488    -.0652176    .1360952
     comform |  -.0266358   .0998894    -0.27   0.790    -.2239075    .1706358
         dom |   .0109832   .0359129     0.31   0.760    -.0599411    .0819076
    econfood |  -.0150852   .0099253    -1.52   0.131    -.0346867    .0045163
       house |   .0333299   .0333682     1.00   0.319    -.0325691    .0992289
      llomue |  -.0993695    .053206    -1.87   0.064    -.2044462    .0057072
     chitsua |  -.0435041   .0887626    -0.49   0.625    -.2188015    .1317933
      living |   .0311387   .0108127     2.88   0.005     .0097847    .0524926
       _cons |    .590924   .0535509    11.03   0.000     .4851663    .6966818
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     943
-------------+------------------------------           F( 25,   917) =    2.82
       Model |  7.05275159    25  .282110064           Prob > F      =  0.0000
    Residual |  91.7962967   917  .100105013           R-squared     =  0.0713
-------------+------------------------------           Adj R-squared =  0.0460
       Total |  98.8490483   942  .104935295           Root MSE      =  .31639

------------------------------------------------------------------------------
        intt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0753457     .02965     2.54   0.011      .017156    .1335354
     hotline |   .0873785   .0294748     2.96   0.003     .0295325    .1452244
     verdade |   .0423494   .0302655     1.40   0.162    -.0170483    .1017471
         pr1 |   .0556034   .0359152     1.55   0.122     -.014882    .1260889
         pr2 |    .072729   .0345302     2.11   0.035     .0049617    .1404963
         pr3 |   .0849818   .0336853     2.52   0.012     .0188725    .1510911
        post |  -.0182908   .0354798    -0.52   0.606    -.0879218    .0513401
   post_miss |   -.015867   .0598383    -0.27   0.791     -.133303     .101569
      health |    .012081   .0247841     0.49   0.626    -.0365592    .0607212
 health_miss |  -.0027855   .0657988    -0.04   0.966    -.1319193    .1263482
         sex |   .0504879    .022272     2.27   0.024     .0067778    .0941981
         age |  -.0006034   .0008648    -0.70   0.486    -.0023006    .0010939
      single |  -.0361656   .0287209    -1.26   0.208    -.0925318    .0202007
       divor |   .1596039   .1220622     1.31   0.191    -.0799499    .3991577
     protest |    .021036   .0252543     0.83   0.405    -.0285269    .0705989
         com |  -.1058584   .0493645    -2.14   0.032     -.202739   -.0089778
        prof |   .1813596    .083647     2.17   0.030     .0171977    .3455215
         tea |   .0354388   .0509297     0.70   0.487    -.0645135    .1353911
     comform |  -.0266358   .0977035    -0.27   0.785    -.2183843    .1651126
         dom |   .0109832   .0319851     0.34   0.731    -.0517893    .0737558
    econfood |  -.0150852   .0091994    -1.64   0.101    -.0331395    .0029691
       house |   .0333299   .0302453     1.10   0.271    -.0260282     .092688
      llomue |  -.0993695    .042026    -2.36   0.018    -.1818479   -.0168912
     chitsua |  -.0435041   .0943348    -0.46   0.645    -.2286412     .141633
      living |   .0311387   .0104553     2.98   0.003     .0106196    .0516577
       _cons |    .590924   .0571085    10.35   0.000     .4788455    .7030026
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     430
                                                       F( 25,   151) =    5.54
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1194
                                                       Root MSE      =   .3191

                                   (Std. Err. adjusted for 152 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        intt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0677552   .0410726     1.65   0.101    -.0133961    .1489064
     hotline |   .0977628   .0364143     2.68   0.008     .0258153    .1697102
     verdade |   .1757798   .0297337     5.91   0.000     .1170319    .2345276
         pr1 |   .0423927   .0529652     0.80   0.425     -.062256    .1470414
         pr2 |    .070144   .0532218     1.32   0.190    -.0350115    .1752996
         pr3 |   .0893403   .0421507     2.12   0.036     .0060589    .1726216
        post |   .0052402    .045685     0.11   0.909    -.0850243    .0955046
   post_miss |  -.0258348   .0795819    -0.32   0.746    -.1830726     .131403
      health |   .0345328   .0288414     1.20   0.233    -.0224521    .0915177
 health_miss |   .1616333   .0660129     2.45   0.015      .031205    .2920616
         sex |    .034869   .0327116     1.07   0.288    -.0297625    .0995006
         age |  -.0006747   .0014724    -0.46   0.647    -.0035839    .0022346
      single |  -.0326418   .0477558    -0.68   0.495    -.1269977    .0617141
       divor |  -.2092253   .2446451    -0.86   0.394    -.6925948    .2741442
     protest |   .0012427   .0375594     0.03   0.974    -.0729671    .0754525
         com |   .0024622   .0758976     0.03   0.974    -.1474962    .1524207
        prof |   .1643348    .047039     3.49   0.001     .0713952    .2572744
         tea |   .0501455   .0655939     0.76   0.446    -.0794549    .1797459
     comform |  -.5239375   .1832441    -2.86   0.005     -.885991    -.161884
         dom |  -.0016937   .0507805    -0.03   0.973    -.1020258    .0986384
    econfood |  -.0108937   .0134161    -0.81   0.418    -.0374012    .0156137
       house |   .0261167    .051251     0.51   0.611     -.075145    .1273783
      llomue |  -.0456998   .0596998    -0.77   0.445    -.1636547     .072255
     chitsua |  -.1155676   .0860108    -1.34   0.181    -.2855076    .0543724
      living |   .0347226   .0175892     1.97   0.050    -.0000301    .0694752
       _cons |   .5719981   .0770441     7.42   0.000     .4197744    .7242217
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     430
-------------+------------------------------           F( 25,   404) =    2.19
       Model |  5.57723025    25   .22308921           Prob > F      =  0.0009
    Residual |  41.1365806   404  .101823219           R-squared     =  0.1194
-------------+------------------------------           Adj R-squared =  0.0649
       Total |  46.7138109   429  .108890002           Root MSE      =   .3191

------------------------------------------------------------------------------
        intt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0677552   .0508353     1.33   0.183    -.0321795    .1676899
     hotline |   .0977628   .0484341     2.02   0.044     .0025484    .1929771
     verdade |   .1757798   .0499583     3.52   0.000      .077569    .2739905
         pr1 |   .0423927   .0552784     0.77   0.444    -.0662765    .1510619
         pr2 |    .070144   .0519955     1.35   0.178    -.0320715    .1723595
         pr3 |   .0893403    .051579     1.73   0.084    -.0120565     .190737
        post |   .0052402   .0457246     0.11   0.909    -.0846477     .095128
   post_miss |  -.0258348   .0777515    -0.33   0.740    -.1786829    .1270133
      health |   .0345328   .0393445     0.88   0.381    -.0428127    .1118783
 health_miss |   .1616333     .11468     1.41   0.159    -.0638108    .3870774
         sex |    .034869   .0335577     1.04   0.299    -.0311005    .1008386
         age |  -.0006747   .0013698    -0.49   0.623    -.0033676    .0020182
      single |  -.0326418   .0410719    -0.79   0.427    -.1133832    .0480996
       divor |  -.2092253   .1879664    -1.11   0.266    -.5787397    .1602891
     protest |   .0012427   .0394644     0.03   0.975    -.0763386     .078824
         com |   .0024622   .0859359     0.03   0.977    -.1664751    .1713996
        prof |   .1643348   .1103219     1.49   0.137    -.0525419    .3812116
         tea |   .0501455   .0674236     0.74   0.457    -.0823994    .1826904
     comform |  -.5239375   .1342408    -3.90   0.000    -.7878351   -.2600398
         dom |  -.0016937   .0474998    -0.04   0.972    -.0950713    .0916839
    econfood |  -.0108937   .0143812    -0.76   0.449    -.0391651    .0173776
       house |   .0261167   .0449217     0.58   0.561    -.0621928    .1144261
      llomue |  -.0456998   .0645519    -0.71   0.479    -.1725994    .0811997
     chitsua |  -.1155676   .1490405    -0.78   0.439    -.4085593    .1774242
      living |   .0347226   .0151741     2.29   0.023     .0048925    .0645526
       _cons |   .5719981   .0850119     6.73   0.000     .4048772    .7391189
------------------------------------------------------------------------------

Simultaneous results for intt_3_2a, intt_3_3a

                                                  Number of obs   =       1106

                                      (Std. Err. adjusted for 161 clusters in ea)
---------------------------------------------------------------------------------
                |               Robust
                |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
intt_3_2a_mean  |
      civiceduc |   .0753457   .0263776     2.86   0.004     .0236465    .1270449
        hotline |   .0873785   .0238448     3.66   0.000     .0406436    .1341133
        verdade |   .0423494   .0308681     1.37   0.170    -.0181509    .1028497
            pr1 |   .0556034   .0300327     1.85   0.064    -.0032595    .1144664
            pr2 |    .072729   .0333491     2.18   0.029     .0073659     .138092
            pr3 |   .0849818   .0315127     2.70   0.007      .023218    .1467456
           post |  -.0182908   .0336676    -0.54   0.587    -.0842781    .0476965
      post_miss |   -.015867   .0597027    -0.27   0.790     -.132882    .1011481
         health |    .012081   .0215409     0.56   0.575    -.0301383    .0543003
    health_miss |  -.0027855   .0564451    -0.05   0.961    -.1134158    .1078448
            sex |   .0504879   .0220765     2.29   0.022     .0072187    .0937572
            age |  -.0006034   .0008881    -0.68   0.497     -.002344    .0011373
         single |  -.0361656   .0335189    -1.08   0.281    -.1018614    .0295303
          divor |   .1596039   .0363105     4.40   0.000     .0884365    .2307713
        protest |    .021036   .0245681     0.86   0.392    -.0271166    .0691887
            com |  -.1058584   .0508603    -2.08   0.037    -.2055428    -.006174
           prof |   .1813596   .0265235     6.84   0.000     .1293746    .2333446
            tea |   .0354388   .0502869     0.70   0.481    -.0631218    .1339993
        comform |  -.0266358    .098555    -0.27   0.787       -.2198    .1665283
            dom |   .0109832   .0354331     0.31   0.757    -.0584644    .0804308
       econfood |  -.0150852   .0097927    -1.54   0.123    -.0342785    .0041082
          house |   .0333299   .0329225     1.01   0.311     -.031197    .0978568
         llomue |  -.0993695   .0524953    -1.89   0.058    -.2022584    .0035193
        chitsua |  -.0435041   .0875768    -0.50   0.619    -.2151516    .1281434
         living |   .0311387   .0106682     2.92   0.004     .0102294     .052048
          _cons |    .590924   .0528355    11.18   0.000     .4873683    .6944797
----------------+----------------------------------------------------------------
intt_3_2a_lnvar |
          _cons |  -2.301536   .0610252   -37.71   0.000    -2.421143   -2.181928
----------------+----------------------------------------------------------------
intt_3_3a_mean  |
      civiceduc |   .0677552   .0398505     1.70   0.089    -.0103504    .1458608
        hotline |   .0977628   .0353309     2.77   0.006     .0285156      .16701
        verdade |   .1757798    .028849     6.09   0.000     .1192367    .2323228
            pr1 |   .0423927   .0513893     0.82   0.409    -.0583284    .1431139
            pr2 |    .070144   .0516382     1.36   0.174     -.031065     .171353
            pr3 |   .0893403   .0408965     2.18   0.029     .0091845     .169496
           post |   .0052402   .0443257     0.12   0.906    -.0816366    .0921169
      post_miss |  -.0258348    .077214    -0.33   0.738    -.1771714    .1255018
         health |   .0345328   .0279833     1.23   0.217    -.0203135     .089379
    health_miss |   .1616333   .0640488     2.52   0.012        .0361    .2871665
            sex |    .034869   .0317383     1.10   0.272    -.0273369    .0970749
            age |  -.0006747   .0014286    -0.47   0.637    -.0034747    .0021254
         single |  -.0326418   .0463349    -0.70   0.481    -.1234565    .0581729
          divor |  -.2092253   .2373658    -0.88   0.378    -.6744537    .2560031
        protest |   .0012427   .0364418     0.03   0.973     -.070182    .0726674
            com |   .0024622   .0736393     0.03   0.973    -.1418682    .1467927
           prof |   .1643348   .0456394     3.60   0.000     .0748833    .2537863
            tea |   .0501455   .0636422     0.79   0.431    -.0745909    .1748819
        comform |  -.5239375   .1777918    -2.95   0.003    -.8724029    -.175472
            dom |  -.0016937   .0492696    -0.03   0.973    -.0982603    .0948729
       econfood |  -.0108937   .0130169    -0.84   0.403    -.0364064    .0146189
          house |   .0261167    .049726     0.53   0.599    -.0713446     .123578
         llomue |  -.0456998   .0579235    -0.79   0.430    -.1592278    .0678281
        chitsua |  -.1155676   .0834516    -1.38   0.166    -.2791297    .0479945
         living |   .0347226   .0170658     2.03   0.042     .0012742    .0681709
          _cons |   .5719981   .0747517     7.65   0.000     .4254874    .7185087
----------------+----------------------------------------------------------------
intt_3_3a_lnvar |
          _cons |  -2.284517   .0794235   -28.76   0.000    -2.440184    -2.12885
---------------------------------------------------------------------------------

 ( 1)  [intt_3_2a_mean]civiceduc - [intt_3_3a_mean]civiceduc = 0

           chi2(  1) =    0.03
         Prob > chi2 =    0.8616
.86163606

 ( 1)  [intt_3_2a_mean]hotline - [intt_3_3a_mean]hotline = 0

           chi2(  1) =    0.09
         Prob > chi2 =    0.7625
.76253824

 ( 1)  [intt_3_2a_mean]verdade - [intt_3_3a_mean]verdade = 0

           chi2(  1) =   18.19
         Prob > chi2 =    0.0000
.00002003

. 
. matrix define means=(m_tresp_2_1, m_tresp_3_1, m_intt_2_1, m_intt_3_1 \ t_tresp_2_1_1, t_tresp
> _3_1_1, t_intt_2_1_1, t_intt_3_1_1 \ t_tresp_2_1_2, t_tresp_3_1_2, t_intt_2_1_2, t_intt_3_1_2 
> \ t_tresp_2_1_3, t_tresp_3_1_3, t_intt_2_1_3, t_intt_3_1_3 \ t_tresp_2_1_4, t_tresp_3_1_4, t_i
> ntt_2_1_4, t_intt_3_1_4 \ t_tresp_2_5, t_tresp_3_5, t_intt_2_5, t_intt_3_5 \ t_tresp_2_6, t_tr
> esp_3_6, t_intt_2_6, t_intt_3_6 \ t_tresp_2_7, t_tresp_3_7, t_intt_2_7, t_intt_3_7)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_turnoutcarta.xml") replace sheet(
> "turnout 1") 


note: results saved to outputregs_turnoutcarta.xml

. xml_tab $list2, save("outputregs_turnoutcarta.xml") append sheet("turnout 1 stats") 


note: results saved to outputregs_turnoutcarta.xml

. estimates clear

. 
. global list1=""

. global list2=""

. 
. foreach i in $turnout2 {
  2. 
.         regress `i' $treat $prov if time==1, cluster(ea)
  3.         estimates store `i'_2_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2_1=r(mean)
  6.         display m_`i'_2_1
  7.         test civiceduc = hotline
  8.         scalar define t_`i'_2_1_1=r(p)
  9.         display t_`i'_2_1_1
 10.         test civiceduc = verdade
 11.         scalar define t_`i'_2_1_2=r(p)
 12.         display t_`i'_2_1_2
 13.         test hotline = verdade
 14.         scalar define t_`i'_2_1_3=r(p)
 15.         display t_`i'_2_1_3
 16.         test civiceduc hotline verdade
 17.         scalar define t_`i'_2_1_4=r(p)
 18.         display t_`i'_2_1_4
 19. 
.         regress `i' $treat $prov if time==1 & lazy==0, cluster(ea)
 20.         estimates store `i'_2_2
 21.         regress `i' $treat $prov if time==1 & lazy==0
 22.         estimates store `i'_2_2a
 23.         
.         regress `i' $treat $prov if time==1 & (lazy==1|control==1), cluster(ea)
 24.         estimates store `i'_2_3
 25.         regress `i' $treat $prov if time==1 & (lazy==1|control==1)
 26.         estimates store `i'_2_3a
 27. 
.         suest `i'_2_2a `i'_2_3a, cluster(ea)
 28.         test [`i'_2_2a_mean]civiceduc=[`i'_2_3a_mean]civiceduc  
 29.         scalar define t_`i'_2_5=r(p)
 30.         display t_`i'_2_5
 31.         test [`i'_2_2a_mean]hotline=[`i'_2_3a_mean]hotline      
 32.         scalar define t_`i'_2_6=r(p)
 33.         display t_`i'_2_6
 34.         test [`i'_2_2a_mean]verdade=[`i'_2_3a_mean]verdade
 35.         scalar define t_`i'_2_7=r(p)
 36.         display t_`i'_2_7
 37. 
.         regress `i' $treat $prov $ea $controls if time==1, cluster(ea)
 38.         estimates store `i'_3_1
 39.         sum `i' if e(sample) & control == 1
 40.         scalar define m_`i'_3_1=r(mean)
 41.         display m_`i'_3_1
 42.         test civiceduc = hotline
 43.         scalar define t_`i'_3_1_1=r(p)
 44.         display t_`i'_3_1_1
 45.         test civiceduc = verdade
 46.         scalar define t_`i'_3_1_2=r(p)
 47.         display t_`i'_3_1_2
 48.         test hotline = verdade
 49.         scalar define t_`i'_3_1_3=r(p)
 50.         display t_`i'_3_1_3
 51.         test civiceduc hotline verdade
 52.         scalar define t_`i'_3_1_4=r(p)
 53.         display t_`i'_3_1_4
 54. 
.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0, cluster(ea)
 55.         estimates store `i'_3_2
 56.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0
 57.         estimates store `i'_3_2a
 58.         
.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1), cluster(ea)
 59.         estimates store `i'_3_3
 60.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1)
 61.         estimates store `i'_3_3a
 62. 
.         suest `i'_3_2a `i'_3_3a, cluster(ea)
 63.         test [`i'_3_2a_mean]civiceduc=[`i'_3_3a_mean]civiceduc  
 64.         scalar define t_`i'_3_5=r(p)
 65.         display t_`i'_3_5
 66.         test [`i'_3_2a_mean]hotline=[`i'_3_3a_mean]hotline      
 67.         scalar define t_`i'_3_6=r(p)
 68.         display t_`i'_3_6
 69.         test [`i'_3_2a_mean]verdade=[`i'_3_3a_mean]verdade
 70.         scalar define t_`i'_3_7=r(p)
 71.         display t_`i'_3_7
 72.         
.         global list1="$list1" + " `i'_2_1" + " `i'_3_1" + " `i'_2_2" + " `i'_3_2"  + " `i'_2_3
> " + " `i'_3_3"
 73.         
.         }

Linear regression                                      Number of obs =    1121
                                                       F(  6,   160) =    5.84
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0191
                                                       Root MSE      =  .35635

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     tfinger |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0550497   .0261239     2.11   0.037     .0034576    .1066418
     hotline |   .0680474   .0257344     2.64   0.009     .0172245    .1188702
     verdade |   .0406451   .0376843     1.08   0.282    -.0337777    .1150679
         pr1 |  -.0094697    .036117    -0.26   0.794    -.0807973    .0618579
         pr2 |  -.0006302   .0275035    -0.02   0.982     -.054947    .0536865
         pr3 |   .0949463   .0238883     3.97   0.000     .0477694    .1421233
       _cons |   .7860219   .0248474    31.63   0.000     .7369507    .8350932
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     tfinger |       269    .8066914    .3956289          0          1
.80669145

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.30
            Prob > F =    0.5822
.58218038

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.16
            Prob > F =    0.6906
.69064488

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.59
            Prob > F =    0.4424
.44244757

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    2.50
            Prob > F =    0.0615
.06151064

Linear regression                                      Number of obs =     953
                                                       F(  6,   160) =    5.44
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0203
                                                       Root MSE      =  .36566

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     tfinger |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0547895   .0285308     1.92   0.057    -.0015559    .1111349
     hotline |   .0625963   .0263362     2.38   0.019     .0105849    .1146077
     verdade |    .013597   .0401317     0.34   0.735    -.0656591    .0928531
         pr1 |   -.004836   .0381522    -0.13   0.899    -.0801828    .0705107
         pr2 |  -.0116087   .0305341    -0.38   0.704    -.0719105     .048693
         pr3 |   .0982456    .027093     3.63   0.000     .0447396    .1517517
       _cons |   .7868227   .0259287    30.35   0.000     .7356162    .8380293
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     953
-------------+------------------------------           F(  6,   946) =    3.27
       Model |  2.62401684     6   .43733614           Prob > F      =  0.0034
    Residual |  126.490359   946  .133710739           R-squared     =  0.0203
-------------+------------------------------           Adj R-squared =  0.0141
       Total |  129.114376   952  .135624344           Root MSE      =  .36566

------------------------------------------------------------------------------
     tfinger |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0547895   .0324709     1.69   0.092    -.0089338    .1185127
     hotline |   .0625963   .0328578     1.91   0.057    -.0018864     .127079
     verdade |    .013597   .0335044     0.41   0.685    -.0521546    .0793485
         pr1 |   -.004836   .0334694    -0.14   0.885    -.0705189    .0608468
         pr2 |  -.0116087   .0333164    -0.35   0.728    -.0769914    .0537739
         pr3 |   .0982456    .033785     2.91   0.004     .0319435    .1645478
       _cons |   .7868227   .0305315    25.77   0.000     .7269054    .8467401
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     437
                                                       F(  6,   152) =    3.94
                                                       Prob > F      =  0.0011
                                                       R-squared     =  0.0282
                                                       Root MSE      =  .36023

                                   (Std. Err. adjusted for 153 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     tfinger |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0568232   .0482365     1.18   0.241    -.0384773    .1521238
     hotline |    .090203   .0426764     2.11   0.036     .0058875    .1745184
     verdade |   .1425998   .0358727     3.98   0.000     .0717263    .2134732
         pr1 |  -.0140967   .0455794    -0.31   0.758    -.1041476    .0759541
         pr2 |  -.0094106   .0375083    -0.25   0.802    -.0835156    .0646944
         pr3 |    .066129   .0350247     1.89   0.061    -.0030691    .1353271
       _cons |   .7965486   .0262771    30.31   0.000     .7446331    .8484642
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     437
-------------+------------------------------           F(  6,   430) =    2.08
       Model |  1.61999285     6  .269998809           Prob > F      =  0.0543
    Residual |  55.7987714   430  .129764585           R-squared     =  0.0282
-------------+------------------------------           Adj R-squared =  0.0147
       Total |  57.4187643   436  .131694414           Root MSE      =  .36023

------------------------------------------------------------------------------
     tfinger |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0568232   .0542021     1.05   0.295    -.0497108    .1633572
     hotline |    .090203   .0518765     1.74   0.083    -.0117601     .192166
     verdade |   .1425998   .0530197     2.69   0.007     .0383897    .2468098
         pr1 |  -.0140967   .0485584    -0.29   0.772     -.109538    .0813445
         pr2 |  -.0094106   .0486652    -0.19   0.847    -.1050618    .0862407
         pr3 |    .066129   .0486857     1.36   0.175    -.0295625    .1618205
       _cons |   .7965486   .0372993    21.36   0.000      .723237    .8698603
------------------------------------------------------------------------------

Simultaneous results for tfinger_2_2a, tfinger_2_3a

                                                  Number of obs   =       1121

                                         (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------------
                   |               Robust
                   |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
tfinger_2_2a_mean  |
         civiceduc |   .0547895   .0284407     1.93   0.054    -.0009533    .1105322
           hotline |   .0625963   .0262531     2.38   0.017     .0111413    .1140514
           verdade |    .013597    .040005     0.34   0.734    -.0648114    .0920054
               pr1 |   -.004836   .0380317    -0.13   0.899    -.0793769    .0697048
               pr2 |  -.0116087   .0304377    -0.38   0.703    -.0712655     .048048
               pr3 |   .0982456   .0270075     3.64   0.000     .0453119    .1511794
             _cons |   .7868227   .0258468    30.44   0.000     .7361639    .8374816
-------------------+----------------------------------------------------------------
tfinger_2_2a_lnvar |
             _cons |  -2.012076   .0607114   -33.14   0.000    -2.131069   -1.893084
-------------------+----------------------------------------------------------------
tfinger_2_3a_mean  |
         civiceduc |   .0568232   .0478956     1.19   0.235    -.0370504    .1506969
           hotline |    .090203   .0423748     2.13   0.033     .0071499     .173256
           verdade |   .1425998   .0356192     4.00   0.000     .0727875    .2124121
               pr1 |  -.0140967   .0452573    -0.31   0.755    -.1027993    .0746059
               pr2 |  -.0094106   .0372433    -0.25   0.801    -.0824061    .0635849
               pr3 |    .066129   .0347772     1.90   0.057    -.0020331    .1342911
             _cons |   .7965486   .0260914    30.53   0.000     .7454103    .8476869
-------------------+----------------------------------------------------------------
tfinger_2_3a_lnvar |
             _cons |  -2.042033   .0822664   -24.82   0.000    -2.203273   -1.880794
------------------------------------------------------------------------------------

 ( 1)  [tfinger_2_2a_mean]civiceduc - [tfinger_2_3a_mean]civiceduc = 0

           chi2(  1) =    0.00
         Prob > chi2 =    0.9689
.96886852

 ( 1)  [tfinger_2_2a_mean]hotline - [tfinger_2_3a_mean]hotline = 0

           chi2(  1) =    0.46
         Prob > chi2 =    0.4998
.49976546

 ( 1)  [tfinger_2_2a_mean]verdade - [tfinger_2_3a_mean]verdade = 0

           chi2(  1) =   20.48
         Prob > chi2 =    0.0000
6.018e-06

Linear regression                                      Number of obs =    1106
                                                       F( 25,   160) =    4.87
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0575
                                                       Root MSE      =  .35263

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     tfinger |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0553561   .0256136     2.16   0.032     .0047718    .1059405
     hotline |   .0532734   .0254091     2.10   0.038     .0030929    .1034538
     verdade |   .0458461   .0332908     1.38   0.170    -.0198999    .1115922
         pr1 |   .0353225   .0326087     1.08   0.280    -.0290764    .0997215
         pr2 |   .0026789    .034962     0.08   0.939    -.0663677    .0717255
         pr3 |   .0625488   .0303362     2.06   0.041     .0026377    .1224598
        post |  -.0516789   .0318513    -1.62   0.107    -.1145821    .0112243
   post_miss |  -.0019868   .0573302    -0.03   0.972    -.1152083    .1112347
      health |   .0298222   .0234527     1.27   0.205    -.0164946     .076139
 health_miss |    .035482   .0632612     0.56   0.576    -.0894527    .1604167
         sex |   .0474122   .0215403     2.20   0.029     .0048723    .0899521
         age |  -.0005424   .0009848    -0.55   0.583    -.0024873    .0014024
      single |  -.0521573     .03232    -1.61   0.109    -.1159861    .0116716
       divor |  -.0522346   .1415327    -0.37   0.713    -.3317478    .2272787
     protest |   .0334701   .0267439     1.25   0.213    -.0193465    .0862867
         com |  -.0090704   .0490358    -0.18   0.853    -.1059112    .0877703
        prof |   .1416533   .0240642     5.89   0.000     .0941289    .1891778
         tea |    -.01388   .0481539    -0.29   0.774    -.1089792    .0812191
     comform |  -.2576188    .116828    -2.21   0.029    -.4883426   -.0268951
         dom |  -.0019485   .0390543    -0.05   0.960    -.0790769      .07518
    econfood |  -.0068833   .0100301    -0.69   0.494    -.0266918    .0129251
       house |   .0336706    .034989     0.96   0.337    -.0354292    .1027703
      llomue |  -.0716798   .0558624    -1.28   0.201    -.1820025    .0386429
     chitsua |  -.0604757   .1022317    -0.59   0.555    -.2623733    .1414219
      living |   .0380483   .0129242     2.94   0.004     .0125244    .0635723
       _cons |   .6350285    .060387    10.52   0.000     .5157701    .7542868
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     tfinger |       267    .8052434    .3967569          0          1
.80524345

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.01
            Prob > F =    0.9329
.9329421

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.08
            Prob > F =    0.7793
.77930013

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.05
            Prob > F =    0.8179
.81793739

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    2.01
            Prob > F =    0.1146
.11461387

Linear regression                                      Number of obs =     943
                                                       F( 25,   160) =    3.95
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0523
                                                       Root MSE      =  .36299

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     tfinger |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0461825   .0290418     1.59   0.114    -.0111721    .1035371
     hotline |   .0489592   .0267509     1.83   0.069    -.0038712    .1017896
     verdade |   .0128883   .0363092     0.35   0.723    -.0588187    .0845953
         pr1 |   .0343916   .0337272     1.02   0.309    -.0322163    .1009996
         pr2 |   -.012124   .0374576    -0.32   0.747     -.086099    .0618511
         pr3 |   .0654641   .0336347     1.95   0.053    -.0009611    .1318892
        post |  -.0706653    .036951    -1.91   0.058    -.1436398    .0023093
   post_miss |  -.0203444   .0561668    -0.36   0.718    -.1312683    .0905795
      health |   .0311864   .0248475     1.26   0.211     -.017885    .0802577
 health_miss |   .0276926   .0697848     0.40   0.692    -.1101255    .1655106
         sex |    .056374   .0238938     2.36   0.020      .009186     .103562
         age |  -.0004942    .001044    -0.47   0.637     -.002556    .0015675
      single |  -.0670033   .0378797    -1.77   0.079    -.1418121    .0078054
       divor |    .051318   .1321862     0.39   0.698    -.2097367    .3123727
     protest |   .0338322    .029762     1.14   0.257    -.0249449    .0926093
         com |  -.0039038    .054749    -0.07   0.943    -.1120277    .1042201
        prof |   .1608027   .0296446     5.42   0.000     .1022576    .2193479
         tea |  -.0366498   .0558073    -0.66   0.512    -.1468638    .0735642
     comform |  -.0984184   .1207542    -0.82   0.416     -.336896    .1400591
         dom |    .016906   .0402004     0.42   0.675    -.0624857    .0962978
    econfood |  -.0065861   .0111889    -0.59   0.557    -.0286832    .0155109
       house |   .0333172   .0400634     0.83   0.407    -.0458041    .1124386
      llomue |  -.0711812   .0593429    -1.20   0.232    -.1883776    .0460152
     chitsua |  -.0838524   .1254335    -0.67   0.505    -.3315712    .1638664
      living |   .0318975   .0130962     2.44   0.016     .0060338    .0577611
       _cons |   .6554348    .065119    10.07   0.000     .5268311    .7840384
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     943
-------------+------------------------------           F( 25,   917) =    2.03
       Model |  6.67393439    25  .266957376           Prob > F      =  0.0022
    Residual |  120.825535   917  .131761762           R-squared     =  0.0523
-------------+------------------------------           Adj R-squared =  0.0265
       Total |   127.49947   942  .135349756           Root MSE      =  .36299

------------------------------------------------------------------------------
     tfinger |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0461825   .0340166     1.36   0.175     -.020577     .112942
     hotline |   .0489592   .0338157     1.45   0.148    -.0174058    .1153243
     verdade |   .0128883   .0347228     0.37   0.711    -.0552571    .0810337
         pr1 |   .0343916   .0412045     0.83   0.404    -.0464744    .1152577
         pr2 |   -.012124   .0396155    -0.31   0.760    -.0898716    .0656236
         pr3 |   .0654641   .0386463     1.69   0.091    -.0103814    .1413095
        post |  -.0706653    .040705    -1.74   0.083     -.150551    .0092204
   post_miss |  -.0203444   .0686509    -0.30   0.767    -.1550756    .1143868
      health |   .0311864   .0284342     1.10   0.273    -.0246172      .08699
 health_miss |   .0276926   .0754892     0.37   0.714    -.1204591    .1758442
         sex |    .056374   .0255521     2.21   0.028     .0062266    .1065214
         age |  -.0004942   .0009922    -0.50   0.619    -.0024415     .001453
      single |  -.0670033   .0329507    -2.03   0.042    -.1316709   -.0023358
       divor |    .051318   .1400387     0.37   0.714    -.2235156    .3261516
     protest |   .0338322   .0289736     1.17   0.243      -.02303    .0906944
         com |  -.0039038   .0566346    -0.07   0.945    -.1150523    .1072447
        prof |   .1608027    .095966     1.68   0.094    -.0275357    .3491412
         tea |  -.0366498   .0584303    -0.63   0.531    -.1513224    .0780228
     comform |  -.0984184   .1120926    -0.88   0.380    -.3184062    .1215694
         dom |    .016906   .0366957     0.46   0.645    -.0551112    .0889233
    econfood |  -.0065861   .0105542    -0.62   0.533    -.0272994    .0141271
       house |   .0333172   .0346996     0.96   0.337    -.0347827    .1014171
      llomue |  -.0711812   .0482153    -1.48   0.140    -.1658064     .023444
     chitsua |  -.0838524   .1082277    -0.77   0.439    -.2962552    .1285504
      living |   .0318975    .011995     2.66   0.008     .0083566    .0554384
       _cons |   .6554348    .065519    10.00   0.000     .5268501    .7840194
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     430
                                                       F( 25,   151) =    7.04
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1305
                                                       Root MSE      =  .35102

                                   (Std. Err. adjusted for 152 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     tfinger |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1022427   .0470821     2.17   0.031     .0092179    .1952675
     hotline |   .0795937   .0445072     1.79   0.076    -.0083436     .167531
     verdade |   .1898572   .0314475     6.04   0.000     .1277232    .2519911
         pr1 |   .0406599   .0486215     0.84   0.404    -.0554064    .1367262
         pr2 |   .0211863   .0521923     0.41   0.685    -.0819353    .1243078
         pr3 |   .0376415   .0452708     0.83   0.407    -.0518045    .1270876
        post |   .0224555   .0431391     0.52   0.603    -.0627787    .1076897
   post_miss |   .0139203   .0660463     0.21   0.833     -.116574    .1444145
      health |   .0488066   .0345365     1.41   0.160    -.0194305    .1170437
 health_miss |   .2037672   .0569257     3.58   0.000     .0912935    .3162408
         sex |   .0205399   .0337357     0.61   0.544    -.0461151    .0871949
         age |  -.0009982   .0018104    -0.55   0.582    -.0045752    .0025787
      single |  -.0825368    .052998    -1.56   0.121    -.1872501    .0221766
       divor |  -.2431098    .259617    -0.94   0.351    -.7560608    .2698412
     protest |   .0044903   .0467706     0.10   0.924    -.0879191    .0968996
         com |   .0907166   .0668157     1.36   0.177    -.0412977    .2227309
        prof |   .1411417   .0357355     3.95   0.000     .0705355    .2117479
         tea |  -.0513174   .0663077    -0.77   0.440    -.1823281    .0796933
     comform |  -.6481587    .173441    -3.74   0.000    -.9908433   -.3054741
         dom |   -.047943   .0573237    -0.84   0.404    -.1612031    .0653171
    econfood |  -.0080938   .0151439    -0.53   0.594    -.0380152    .0218275
       house |   .0509476   .0584483     0.87   0.385    -.0645344    .1664297
      llomue |  -.0333163   .0615264    -0.54   0.589    -.1548801    .0882476
     chitsua |  -.0919428   .1327871    -0.69   0.490    -.3543034    .1704179
      living |   .0466661   .0221007     2.11   0.036     .0029995    .0903326
       _cons |    .604775   .0877221     6.89   0.000     .4314538    .7780962
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     430
-------------+------------------------------           F( 25,   404) =    2.42
       Model |  7.46864744    25  .298745897           Prob > F      =  0.0002
    Residual |  49.7778642   404  .123212535           R-squared     =  0.1305
-------------+------------------------------           Adj R-squared =  0.0767
       Total |  57.2465116   429  .133441752           Root MSE      =  .35102

------------------------------------------------------------------------------
     tfinger |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1022427   .0559203     1.83   0.068    -.0076883    .2121737
     hotline |   .0795937   .0532789     1.49   0.136    -.0251448    .1843322
     verdade |   .1898572   .0549556     3.45   0.001     .0818225    .2978918
         pr1 |   .0406599   .0608078     0.67   0.504    -.0788793    .1601991
         pr2 |   .0211863   .0571965     0.37   0.711    -.0912537    .1336263
         pr3 |   .0376415   .0567384     0.66   0.507    -.0738978    .1491809
        post |   .0224555   .0502983     0.45   0.656    -.0764237    .1213346
   post_miss |   .0139203   .0855289     0.16   0.871     -.154217    .1820575
      health |   .0488066   .0432801     1.13   0.260    -.0362756    .1338889
 health_miss |   .2037672   .1261513     1.62   0.107    -.0442278    .4517622
         sex |   .0205399   .0369144     0.56   0.578    -.0520285    .0931083
         age |  -.0009982   .0015069    -0.66   0.508    -.0039605     .001964
      single |  -.0825368   .0451803    -1.83   0.068    -.1713546    .0062811
       divor |  -.2431098   .2067685    -1.18   0.240    -.6495863    .1633667
     protest |   .0044903    .043412     0.10   0.918    -.0808514    .0898319
         com |   .0907166    .094532     0.96   0.338    -.0951194    .2765526
        prof |   .1411417   .1213573     1.16   0.246     -.097429    .3797123
         tea |  -.0513174   .0741679    -0.69   0.489    -.1971205    .0944858
     comform |  -.6481587   .1476687    -4.39   0.000    -.9384537   -.3578637
         dom |   -.047943   .0522511    -0.92   0.359     -.150661    .0547751
    econfood |  -.0080938   .0158198    -0.51   0.609    -.0391932    .0230055
       house |   .0509476   .0494151     1.03   0.303    -.0461953    .1480906
      llomue |  -.0333163   .0710089    -0.47   0.639    -.1729094    .1062769
     chitsua |  -.0919428   .1639488    -0.56   0.575    -.4142421    .2303566
      living |   .0466661   .0166919     2.80   0.005     .0138522      .07948
       _cons |    .604775   .0935155     6.47   0.000     .4209373    .7886128
------------------------------------------------------------------------------

Simultaneous results for tfinger_3_2a, tfinger_3_3a

                                                  Number of obs   =       1106

                                         (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------------
                   |               Robust
                   |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
tfinger_3_2a_mean  |
         civiceduc |   .0461825   .0286538     1.61   0.107    -.0099779    .1023429
           hotline |   .0489592   .0263935     1.85   0.064    -.0027712    .1006896
           verdade |   .0128883   .0358241     0.36   0.719    -.0573257    .0831022
               pr1 |   .0343916   .0332767     1.03   0.301    -.0308295    .0996127
               pr2 |   -.012124   .0369572    -0.33   0.743    -.0845588    .0603108
               pr3 |   .0654641   .0331854     1.97   0.049      .000422    .1305062
              post |  -.0706653   .0364574    -1.94   0.053    -.1421204    .0007898
         post_miss |  -.0203444   .0554165    -0.37   0.714    -.1289587    .0882699
            health |   .0311864   .0245156     1.27   0.203    -.0168632     .079236
       health_miss |   .0276926   .0688525     0.40   0.688    -.1072559     .162641
               sex |    .056374   .0235747     2.39   0.017     .0101685    .1025795
               age |  -.0004942     .00103    -0.48   0.631    -.0025131    .0015246
            single |  -.0670033   .0373737    -1.79   0.073    -.1402544    .0062478
             divor |    .051318   .1304203     0.39   0.694    -.2043011    .3069372
           protest |   .0338322   .0293644     1.15   0.249     -.023721    .0913855
               com |  -.0039038   .0540176    -0.07   0.942    -.1097764    .1019688
              prof |   .1608027   .0292486     5.50   0.000     .1034766    .2181289
               tea |  -.0366498   .0550618    -0.67   0.506     -.144569    .0712694
           comform |  -.0984184    .119141    -0.83   0.409    -.3319305    .1350937
               dom |    .016906   .0396633     0.43   0.670    -.0608327    .0946447
          econfood |  -.0065861   .0110395    -0.60   0.551    -.0282231    .0150508
             house |   .0333172   .0395282     0.84   0.399    -.0441567    .1107911
            llomue |  -.0711812   .0585502    -1.22   0.224    -.1859374     .043575
           chitsua |  -.0838524   .1237578    -0.68   0.498    -.3264133    .1587085
            living |   .0318975   .0129212     2.47   0.014     .0065724    .0572226
             _cons |   .6554348   .0642491    10.20   0.000     .5295089    .7813607
-------------------+----------------------------------------------------------------
tfinger_3_2a_lnvar |
             _cons |   -2.02676   .0574639   -35.27   0.000    -2.139387   -1.914133
-------------------+----------------------------------------------------------------
tfinger_3_3a_mean  |
         civiceduc |   .1022427   .0456812     2.24   0.025     .0127091    .1917763
           hotline |   .0795937   .0431829     1.84   0.065    -.0050433    .1642307
           verdade |   .1898572   .0305118     6.22   0.000     .1300551    .2496592
               pr1 |   .0406599   .0471748     0.86   0.389     -.051801    .1331208
               pr2 |   .0211863   .0506394     0.42   0.676    -.0780651    .1204376
               pr3 |   .0376415   .0439238     0.86   0.391    -.0484476    .1237307
              post |   .0224555   .0418555     0.54   0.592    -.0595798    .1044908
         post_miss |   .0139203   .0640812     0.22   0.828    -.1116765     .139517
            health |   .0488066   .0335088     1.46   0.145    -.0168695    .1144828
       health_miss |   .2037672   .0552319     3.69   0.000     .0955147    .3120197
               sex |   .0205399   .0327319     0.63   0.530    -.0436135    .0846933
               age |  -.0009982   .0017565    -0.57   0.570    -.0044409    .0024445
            single |  -.0825368   .0514211    -1.61   0.108    -.1833202    .0182467
             divor |  -.2431098   .2518923    -0.97   0.334    -.7368096      .25059
           protest |   .0044903    .045379     0.10   0.921    -.0844509    .0934314
               com |   .0907166   .0648276     1.40   0.162    -.0363431    .2177764
              prof |   .1411417   .0346722     4.07   0.000     .0731854     .209098
               tea |  -.0513174   .0643348    -0.80   0.425    -.1774112    .0747764
           comform |  -.6481587   .1682804    -3.85   0.000    -.9779822   -.3183352
               dom |   -.047943    .055618    -0.86   0.389    -.1569524    .0610664
          econfood |  -.0080938   .0146933    -0.55   0.582    -.0368922    .0207045
             house |   .0509476   .0567092     0.90   0.369    -.0602003    .1620956
            llomue |  -.0333163   .0596957    -0.56   0.577    -.1503178    .0836852
           chitsua |  -.0919428   .1288361    -0.71   0.475    -.3444569    .1605714
            living |   .0466661   .0214431     2.18   0.030     .0046384    .0886938
             _cons |    .604775    .085112     7.11   0.000     .4379586    .7715914
-------------------+----------------------------------------------------------------
tfinger_3_3a_lnvar |
             _cons |  -2.093844   .0787664   -26.58   0.000    -2.248224   -1.939465
------------------------------------------------------------------------------------

 ( 1)  [tfinger_3_2a_mean]civiceduc - [tfinger_3_3a_mean]civiceduc = 0

           chi2(  1) =    1.21
         Prob > chi2 =    0.2712
.27121246

 ( 1)  [tfinger_3_2a_mean]hotline - [tfinger_3_3a_mean]hotline = 0

           chi2(  1) =    0.50
         Prob > chi2 =    0.4804
.4804103

 ( 1)  [tfinger_3_2a_mean]verdade - [tfinger_3_3a_mean]verdade = 0

           chi2(  1) =   29.44
         Prob > chi2 =    0.0000
5.755e-08

Linear regression                                      Number of obs =    1121
                                                       F(  6,   160) =    8.48
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0443
                                                       Root MSE      =  .44535

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0518551   .0322821     1.61   0.110     -.011899    .1156091
     hotline |    .083972   .0388832     2.16   0.032     .0071815    .1607625
     verdade |    .084117   .0400153     2.10   0.037     .0050907    .1631432
         pr1 |   .1249378   .0335775     3.72   0.000     .0586255      .19125
         pr2 |   .1728214   .0408503     4.23   0.000      .092146    .2534968
         pr3 |   .2480767   .0390758     6.35   0.000     .1709059    .3252474
       _cons |   .1002105   .0318267     3.15   0.002     .0373559     .163065
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tseen |       269    .2379182    .4266025          0          1
.23791822

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.71
            Prob > F =    0.4004
.40041842

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.67
            Prob > F =    0.4130
.41295015

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.00
            Prob > F =    0.9974
.99741416

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    2.26
            Prob > F =    0.0839
.08393051

Linear regression                                      Number of obs =     953
                                                       F(  6,   160) =    5.80
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0393
                                                       Root MSE      =  .44717

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0621176   .0338516     1.83   0.068     -.004736    .1289713
     hotline |   .0860276   .0437272     1.97   0.051    -.0003292    .1723844
     verdade |    .085205   .0448442     1.90   0.059    -.0033579    .1737678
         pr1 |   .1173476   .0359391     3.27   0.001     .0463714    .1883237
         pr2 |     .13205   .0457417     2.89   0.004     .0417147    .2223853
         pr3 |   .2364672   .0454527     5.20   0.000     .1467026    .3262318
       _cons |   .1154921   .0337805     3.42   0.001     .0487789    .1822053
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     953
-------------+------------------------------           F(  6,   946) =    6.45
       Model |  7.74037934     6  1.29006322           Prob > F      =  0.0000
    Residual |  189.164133   946  .199962085           R-squared     =  0.0393
-------------+------------------------------           Adj R-squared =  0.0332
       Total |  196.904512   952  .206832471           Root MSE      =  .44717

------------------------------------------------------------------------------
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0621176   .0397086     1.56   0.118    -.0158095    .1400448
     hotline |   .0860276   .0401818     2.14   0.033     .0071718    .1648834
     verdade |    .085205   .0409725     2.08   0.038     .0047974    .1656125
         pr1 |   .1173476   .0409297     2.87   0.004      .037024    .1976711
         pr2 |     .13205   .0407426     3.24   0.001     .0520936    .2120063
         pr3 |   .2364672   .0413156     5.72   0.000     .1553864    .3175481
       _cons |   .1154921    .037337     3.09   0.002     .0422193     .188765
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     437
                                                       F(  6,   152) =    6.36
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0663
                                                       Root MSE      =  .42528

                                   (Std. Err. adjusted for 153 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0031567   .0664105     0.05   0.962    -.1280502    .1343635
     hotline |   .0791271   .0634824     1.25   0.215    -.0462946    .2045489
     verdade |   .0806875   .0736522     1.10   0.275    -.0648267    .2262017
         pr1 |   .1462257   .0495215     2.95   0.004     .0483863     .244065
         pr2 |   .2116046   .0586588     3.61   0.000     .0957127    .3274965
         pr3 |   .2963294   .0512221     5.79   0.000     .1951303    .3975285
       _cons |   .0728838   .0360842     2.02   0.045     .0015925    .1441752
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     437
-------------+------------------------------           F(  6,   430) =    5.09
       Model |  5.52584767     6  .920974612           Prob > F      =  0.0000
    Residual |  77.7693468   430  .180858946           R-squared     =  0.0663
-------------+------------------------------           Adj R-squared =  0.0533
       Total |  83.2951945   436  .191044024           Root MSE      =  .42528

------------------------------------------------------------------------------
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0031567   .0639894     0.05   0.961    -.1226142    .1289276
     hotline |   .0791271   .0612439     1.29   0.197    -.0412475    .1995017
     verdade |   .0806875   .0625935     1.29   0.198    -.0423398    .2037148
         pr1 |   .1462257   .0573266     2.55   0.011     .0335505    .2589008
         pr2 |   .2116046   .0574527     3.68   0.000     .0986816    .3245277
         pr3 |   .2963294   .0574769     5.16   0.000     .1833588    .4092999
       _cons |   .0728838   .0440345     1.66   0.099    -.0136658    .1594334
------------------------------------------------------------------------------

Simultaneous results for tseen_2_2a, tseen_2_3a

                                                  Number of obs   =       1121

                                       (Std. Err. adjusted for 161 clusters in ea)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
tseen_2_2a_mean  |
       civiceduc |   .0621176   .0337448     1.84   0.066     -.004021    .1282563
         hotline |   .0860276   .0435891     1.97   0.048     .0005945    .1714608
         verdade |    .085205   .0447027     1.91   0.057    -.0024106    .1728206
             pr1 |   .1173476   .0358257     3.28   0.001     .0471306    .1875646
             pr2 |     .13205   .0455973     2.90   0.004     .0426809    .2214191
             pr3 |   .2364672   .0453092     5.22   0.000     .1476628    .3252717
           _cons |   .1154921   .0336739     3.43   0.001     .0494925    .1814918
-----------------+----------------------------------------------------------------
tseen_2_2a_lnvar |
           _cons |  -1.609628   .0357407   -45.04   0.000    -1.679678   -1.539577
-----------------+----------------------------------------------------------------
tseen_2_3a_mean  |
       civiceduc |   .0031567   .0659412     0.05   0.962    -.1260857     .132399
         hotline |   .0791271   .0630338     1.26   0.209    -.0444168     .202671
         verdade |   .0806875   .0731317     1.10   0.270     -.062648     .224023
             pr1 |   .1462257   .0491716     2.97   0.003     .0498512    .2426002
             pr2 |   .2116046   .0582443     3.63   0.000     .0974479    .3257613
             pr3 |   .2963294   .0508601     5.83   0.000     .1966455    .3960133
           _cons |   .0728838   .0358292     2.03   0.042     .0026599    .1431078
-----------------+----------------------------------------------------------------
tseen_2_3a_lnvar |
           _cons |  -1.710038   .0600481   -28.48   0.000     -1.82773   -1.592346
----------------------------------------------------------------------------------

 ( 1)  [tseen_2_2a_mean]civiceduc - [tseen_2_3a_mean]civiceduc = 0

           chi2(  1) =    0.75
         Prob > chi2 =    0.3867
.38668868

 ( 1)  [tseen_2_2a_mean]hotline - [tseen_2_3a_mean]hotline = 0

           chi2(  1) =    0.01
         Prob > chi2 =    0.9227
.92266105

 ( 1)  [tseen_2_2a_mean]verdade - [tseen_2_3a_mean]verdade = 0

           chi2(  1) =    0.00
         Prob > chi2 =    0.9565
.95650734

Linear regression                                      Number of obs =    1106
                                                       F( 25,   160) =    4.47
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0753
                                                       Root MSE      =  .44328

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0567495   .0336732     1.69   0.094    -.0097518    .1232508
     hotline |   .0733936   .0382041     1.92   0.056    -.0020556    .1488429
     verdade |   .0957018    .041723     2.29   0.023     .0133029    .1781007
         pr1 |   .1537214   .0383848     4.00   0.000     .0779152    .2295275
         pr2 |   .1643576   .0488243     3.37   0.001     .0679344    .2607809
         pr3 |   .2273524    .045341     5.01   0.000     .1378084    .3168965
        post |  -.0253834   .0478082    -0.53   0.596    -.1197999    .0690332
   post_miss |  -.0301575   .0411355    -0.73   0.465     -.111396     .051081
      health |   .0431686   .0344071     1.25   0.211     -.024782    .1111192
 health_miss |   .0770796   .0429156     1.80   0.074    -.0076746    .1618338
         sex |   .0416053   .0309607     1.34   0.181    -.0195391    .1027497
         age |   .0035963   .0012077     2.98   0.003     .0012112    .0059814
      single |  -.0362482   .0321357    -1.13   0.261    -.0997131    .0272167
       divor |   -.200841    .108562    -1.85   0.066    -.4152403    .0135582
     protest |   .0061132   .0333327     0.18   0.855    -.0597156    .0719421
         com |  -.0217074   .0687442    -0.32   0.753    -.1574704    .1140557
        prof |   .2544017   .1400478     1.82   0.071    -.0221789    .5309823
         tea |   .0426839   .0690664     0.62   0.537    -.0937155    .1790833
     comform |  -.0150135   .1177833    -0.13   0.899    -.2476238    .2175968
         dom |   .0125065    .036594     0.34   0.733     -.059763    .0847759
    econfood |  -.0001637   .0134161    -0.01   0.990    -.0266592    .0263319
       house |   .0045858   .0384757     0.12   0.905    -.0713999    .0805715
      llomue |   -.031326   .0550042    -0.57   0.570    -.1399538    .0773018
     chitsua |   -.098053   .1134685    -0.86   0.389    -.3221421     .126036
      living |   .0135333   .0113977     1.19   0.237     -.008976    .0360426
       _cons |  -.1195095   .0768936    -1.55   0.122    -.2713669    .0323479
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tseen |       267    .2397004    .4277023          0          1
.23970037

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.19
            Prob > F =    0.6612
.661182

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.98
            Prob > F =    0.3240
.32399067

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.24
            Prob > F =    0.6241
.62406441

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    2.25
            Prob > F =    0.0848
.08475469

Linear regression                                      Number of obs =     943
                                                       F( 25,   160) =    3.50
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0747
                                                       Root MSE      =  .44478

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0761127   .0362556     2.10   0.037     .0045115    .1477139
     hotline |   .0867511   .0438247     1.98   0.049     .0002017    .1733006
     verdade |   .1048028   .0456918     2.29   0.023      .014566    .1950396
         pr1 |   .1421398   .0409313     3.47   0.001     .0613047     .222975
         pr2 |   .1136071   .0516431     2.20   0.029     .0116171    .2155971
         pr3 |   .2106845   .0532497     3.96   0.000     .1055216    .3158474
        post |   .0164617   .0527068     0.31   0.755    -.0876291    .1205525
   post_miss |  -.0844098   .0561874    -1.50   0.135    -.1953743    .0265547
      health |   .0549353   .0395196     1.39   0.166    -.0231119    .1329826
 health_miss |   .1281317   .0449209     2.85   0.005     .0394175     .216846
         sex |   .0281212   .0334339     0.84   0.402    -.0379074    .0941498
         age |    .003817   .0012864     2.97   0.003     .0012766    .0063575
      single |  -.0389426   .0351667    -1.11   0.270    -.1083933     .030508
       divor |  -.2035235   .1422321    -1.43   0.154    -.4844179    .0773709
     protest |   .0121202   .0388368     0.31   0.755    -.0645788    .0888191
         com |  -.0446112   .0715278    -0.62   0.534    -.1858715    .0966491
        prof |   .2892614   .1480048     1.95   0.052    -.0030334    .5815563
         tea |  -.0037745   .0753595    -0.05   0.960    -.1526021    .1450531
     comform |   .0379886   .1401445     0.27   0.787    -.2387829    .3147602
         dom |  -.0010474   .0395349    -0.03   0.979     -.079125    .0770302
    econfood |  -.0064227   .0143024    -0.45   0.654    -.0346686    .0218231
       house |   .0072313   .0409732     0.18   0.860    -.0736868    .0881493
      llomue |  -.0045523   .0613896    -0.07   0.941    -.1257909    .1166862
     chitsua |  -.1927493   .1170318    -1.65   0.102    -.4238756    .0383771
      living |   .0094561   .0135976     0.70   0.488    -.0173978    .0363101
       _cons |  -.1032081   .0808033    -1.28   0.203    -.2627867    .0563705
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     943
-------------+------------------------------           F( 25,   917) =    2.96
       Model |  14.6365714    25  .585462855           Prob > F      =  0.0000
    Residual |  181.407967   917  .197827663           R-squared     =  0.0747
-------------+------------------------------           Adj R-squared =  0.0494
       Total |  196.044539   942  .208115222           Root MSE      =  .44478

------------------------------------------------------------------------------
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0761127   .0416812     1.83   0.068    -.0056889    .1579143
     hotline |   .0867511   .0414349     2.09   0.037     .0054328    .1680694
     verdade |   .1048028   .0425465     2.46   0.014      .021303    .1883026
         pr1 |   .1421398   .0504886     2.82   0.005     .0430532    .2412265
         pr2 |   .1136071   .0485416     2.34   0.019     .0183416    .2088727
         pr3 |   .2106845    .047354     4.45   0.000     .1177497    .3036193
        post |   .0164617   .0498765     0.33   0.741    -.0814237    .1143471
   post_miss |  -.0844098   .0841192    -1.00   0.316    -.2494983    .0806787
      health |   .0549353   .0348409     1.58   0.115    -.0134418    .1233125
 health_miss |   .1281317   .0924983     1.39   0.166    -.0534012    .3096646
         sex |   .0281212   .0313095     0.90   0.369    -.0333253    .0895678
         age |    .003817   .0012158     3.14   0.002      .001431     .006203
      single |  -.0389426   .0403751    -0.96   0.335    -.1181809    .0402956
       divor |  -.2035235    .171592    -1.19   0.236    -.5402821     .133235
     protest |   .0121202   .0355018     0.34   0.733    -.0575541    .0817944
         com |  -.0446112   .0693954    -0.64   0.520    -.1808035     .091581
        prof |   .2892614   .1175889     2.46   0.014     .0584869     .520036
         tea |  -.0037745   .0715957    -0.05   0.958    -.1442849    .1367359
     comform |   .0379886   .1373491     0.28   0.782    -.2315664    .3075437
         dom |  -.0010474   .0449639    -0.02   0.981    -.0892914    .0871966
    econfood |  -.0064227   .0129323    -0.50   0.620     -.031803    .0189576
       house |   .0072313   .0425181     0.17   0.865    -.0762128    .0906753
      llomue |  -.0045523   .0590791    -0.08   0.939    -.1204983    .1113936
     chitsua |  -.1927493   .1326134    -1.45   0.146    -.4530103    .0675117
      living |   .0094561   .0146977     0.64   0.520     -.019389    .0383012
       _cons |  -.1032081   .0802817    -1.29   0.199    -.2607652     .054349
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     430
                                                       F( 25,   151) =    4.58
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1147
                                                       Root MSE      =  .42479

                                   (Std. Err. adjusted for 152 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0069949   .0664755    -0.11   0.916    -.1383371    .1243472
     hotline |   .0537895   .0675412     0.80   0.427    -.0796584    .1872373
     verdade |   .0799806   .0770032     1.04   0.301    -.0721623    .2321234
         pr1 |   .1408263   .0636046     2.21   0.028     .0151563    .2664962
         pr2 |   .2281122   .0722948     3.16   0.002     .0852721    .3709522
         pr3 |   .3115982   .0589466     5.29   0.000     .1951316    .4280649
        post |  -.0125089   .0638987    -0.20   0.845    -.1387599     .113742
   post_miss |   .0055592   .0713438     0.08   0.938    -.1354018    .1465203
      health |   .0038741   .0622213     0.06   0.950    -.1190627     .126811
 health_miss |   .0134429   .1129354     0.12   0.905    -.2096947    .2365804
         sex |   .0535784    .047304     1.13   0.259    -.0398848    .1470416
         age |   .0028157   .0017963     1.57   0.119    -.0007336    .0063649
      single |  -.0206235   .0486155    -0.42   0.672    -.1166779     .075431
       divor |  -.1948883   .0741636    -2.63   0.009    -.3414207   -.0483558
     protest |   -.013249   .0558651    -0.24   0.813    -.1236272    .0971293
         com |   .0148707   .0917664     0.16   0.871    -.1664413    .1961828
        prof |   .4328156   .1873711     2.31   0.022     .0626081    .8030232
         tea |   .1112501    .097223     1.14   0.254    -.0808429    .3033431
     comform |  -.0255725   .1636169    -0.16   0.876    -.3488466    .2977016
         dom |   .0437184   .0649492     0.67   0.502    -.0846082     .172045
    econfood |   .0178115   .0197471     0.90   0.369    -.0212048    .0568278
       house |   .0516209   .0564639     0.91   0.362    -.0599404    .1631822
      llomue |  -.0067607   .0981082    -0.07   0.945    -.2006029    .1870814
     chitsua |    .086641    .222278     0.39   0.697    -.3525356    .5258176
      living |   .0176466   .0159054     1.11   0.269    -.0137792    .0490724
       _cons |  -.1852092   .1082997    -1.71   0.089    -.3991877    .0287693
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     430
-------------+------------------------------           F( 25,   404) =    2.09
       Model |  9.44632097    25  .377852839           Prob > F      =  0.0018
    Residual |  72.9001907   404  .180446016           R-squared     =  0.1147
-------------+------------------------------           Adj R-squared =  0.0599
       Total |  82.3465116   429  .191949911           Root MSE      =  .42479

------------------------------------------------------------------------------
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0069949    .067673    -0.10   0.918    -.1400301    .1260402
     hotline |   .0537895   .0644765     0.83   0.405    -.0729619    .1805408
     verdade |   .0799806   .0665056     1.20   0.230    -.0507597    .2107208
         pr1 |   .1408263   .0735878     1.91   0.056    -.0038365     .285489
         pr2 |   .2281122   .0692175     3.30   0.001     .0920407    .3641836
         pr3 |   .3115982   .0686631     4.54   0.000     .1766167    .4465798
        post |  -.0125089   .0608695    -0.21   0.837    -.1321695    .1071516
   post_miss |   .0055592   .1035045     0.05   0.957    -.1979153    .2090338
      health |   .0038741   .0523762     0.07   0.941    -.0990898    .1068381
 health_miss |   .0134429   .1526645     0.09   0.930    -.2866731    .3135589
         sex |   .0535784   .0446727     1.20   0.231    -.0342416    .1413985
         age |   .0028157   .0018236     1.54   0.123    -.0007692    .0064005
      single |  -.0206235   .0546758    -0.38   0.706    -.1281081    .0868612
       divor |  -.1948883   .2502249    -0.78   0.437    -.6867938    .2970172
     protest |   -.013249   .0525359    -0.25   0.801    -.1165268    .0900289
         com |   .0148707   .1143997     0.13   0.897    -.2100223    .2397638
        prof |   .4328156   .1468629     2.95   0.003     .1441047    .7215266
         tea |   .1112501   .0897557     1.24   0.216    -.0651965    .2876966
     comform |  -.0255725   .1787041    -0.14   0.886    -.3768786    .3257336
         dom |   .0437184   .0632327     0.69   0.490    -.0805878    .1680247
    econfood |   .0178115   .0191446     0.93   0.353     -.019824    .0554469
       house |   .0516209   .0598007     0.86   0.389    -.0659385    .1691803
      llomue |  -.0067607   .0859329    -0.08   0.937    -.1756921    .1621706
     chitsua |    .086641   .1984059     0.44   0.663    -.3033958    .4766778
      living |   .0176466   .0202001     0.87   0.383    -.0220638     .057357
       _cons |  -.1852092   .1131696    -1.64   0.103     -.407684    .0372656
------------------------------------------------------------------------------

Simultaneous results for tseen_3_2a, tseen_3_3a

                                                  Number of obs   =       1106

                                       (Std. Err. adjusted for 161 clusters in ea)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
tseen_3_2a_mean  |
       civiceduc |   .0761127   .0357713     2.13   0.033     .0060023    .1462231
         hotline |   .0867511   .0432393     2.01   0.045     .0020037    .1714985
         verdade |   .1048028   .0450814     2.32   0.020     .0164449    .1931607
             pr1 |   .1421398   .0403845     3.52   0.000     .0629878    .2212919
             pr2 |   .1136071   .0509532     2.23   0.026     .0137407    .2134735
             pr3 |   .2106845   .0525383     4.01   0.000     .1077113    .3136578
            post |   .0164617   .0520027     0.32   0.752    -.0854618    .1183852
       post_miss |  -.0844098   .0554368    -1.52   0.128    -.1930639    .0242442
          health |   .0549353   .0389916     1.41   0.159    -.0214869    .1313575
     health_miss |   .1281317   .0443208     2.89   0.004     .0412646    .2149989
             sex |   .0281212   .0329872     0.85   0.394    -.0365326     .092775
             age |    .003817   .0012692     3.01   0.003     .0013295    .0063046
          single |  -.0389426   .0346969    -1.12   0.262    -.1069472     .029062
           divor |  -.2035235    .140332    -1.45   0.147    -.4785693    .0715222
         protest |   .0121202    .038318     0.32   0.752    -.0629818    .0872221
             com |  -.0446112   .0705722    -0.63   0.527    -.1829303    .0937078
            prof |   .2892614   .1460276     1.98   0.048     .0030526    .5754703
             tea |  -.0037745   .0743528    -0.05   0.960    -.1495033    .1419543
         comform |   .0379886   .1382723     0.27   0.784    -.2330201    .3089974
             dom |  -.0010474   .0390068    -0.03   0.979    -.0774993    .0754045
        econfood |  -.0064227   .0141113    -0.46   0.649    -.0340804     .021235
           house |   .0072313   .0404259     0.18   0.858     -.072002    .0864645
          llomue |  -.0045523   .0605696    -0.08   0.940    -.1232665    .1141618
         chitsua |  -.1927493   .1154684    -1.67   0.095    -.4190632    .0335647
          living |   .0094561    .013416     0.70   0.481    -.0168387    .0357509
           _cons |  -.1032081   .0797239    -1.29   0.195    -.2594641    .0530478
-----------------+----------------------------------------------------------------
tseen_3_2a_lnvar |
           _cons |  -1.620359   .0352248   -46.00   0.000    -1.689398    -1.55132
-----------------+----------------------------------------------------------------
tseen_3_3a_mean  |
       civiceduc |  -.0069949   .0644975    -0.11   0.914    -.1334077    .1194179
         hotline |   .0537895   .0655315     0.82   0.412      -.07465    .1822289
         verdade |   .0799806    .074712     1.07   0.284    -.0664523    .2264134
             pr1 |   .1408263   .0617121     2.28   0.022     .0198727    .2617798
             pr2 |   .2281122   .0701437     3.25   0.001      .090633    .3655914
             pr3 |   .3115982   .0571927     5.45   0.000     .1995026    .4236938
            post |  -.0125089   .0619974    -0.20   0.840    -.1340216    .1090038
       post_miss |   .0055592    .069221     0.08   0.936    -.1301115      .14123
          health |   .0038741     .06037     0.06   0.949    -.1144489    .1221971
     health_miss |   .0134429    .109575     0.12   0.902    -.2013202     .228206
             sex |   .0535784   .0458965     1.17   0.243     -.036377    .1435339
             age |   .0028157   .0017429     1.62   0.106    -.0006003    .0062317
          single |  -.0206235    .047169    -0.44   0.662    -.1130729     .071826
           divor |  -.1948883   .0719569    -2.71   0.007    -.3359213   -.0538553
         protest |   -.013249   .0542029    -0.24   0.807    -.1194847    .0929868
             com |   .0148707    .089036     0.17   0.867    -.1596366     .189378
            prof |   .4328156   .1817959     2.38   0.017     .0765022    .7891291
             tea |   .1112501   .0943301     1.18   0.238    -.0736336    .2961337
         comform |  -.0255725   .1587486    -0.16   0.872     -.336714    .2855689
             dom |   .0437184   .0630167     0.69   0.488     -.079792    .1672288
        econfood |   .0178115   .0191595     0.93   0.353    -.0197405    .0553635
           house |   .0516209   .0547839     0.94   0.346    -.0557535    .1589953
          llomue |  -.0067607   .0951891    -0.07   0.943    -.1933279    .1798064
         chitsua |    .086641   .2156642     0.40   0.688    -.3360531    .5093351
          living |   .0176466   .0154321     1.14   0.253    -.0125998     .047893
           _cons |  -.1852092   .1050773    -1.76   0.078     -.391157    .0207386
-----------------+----------------------------------------------------------------
tseen_3_3a_lnvar |
           _cons |  -1.712324   .0598615   -28.60   0.000     -1.82965   -1.594997
----------------------------------------------------------------------------------

 ( 1)  [tseen_3_2a_mean]civiceduc - [tseen_3_3a_mean]civiceduc = 0

           chi2(  1) =    1.53
         Prob > chi2 =    0.2166
.21661388

 ( 1)  [tseen_3_2a_mean]hotline - [tseen_3_3a_mean]hotline = 0

           chi2(  1) =    0.20
         Prob > chi2 =    0.6561
.65611713

 ( 1)  [tseen_3_2a_mean]verdade - [tseen_3_3a_mean]verdade = 0

           chi2(  1) =    0.09
         Prob > chi2 =    0.7596
.75957254

. 
. matrix define means=(m_tfinger_2_1, m_tfinger_3_1, m_tseen_2_1, m_tseen_3_1 \ t_tfinger_2_1_1,
>  t_tfinger_3_1_1, t_tseen_2_1_1, t_tseen_3_1_1 \ t_tfinger_2_1_2, t_tfinger_3_1_2, t_tseen_2_1
> _2, t_tseen_3_1_2 \ t_tfinger_2_1_3, t_tfinger_3_1_3, t_tseen_2_1_3, t_tseen_3_1_3 \ t_tfinger
> _2_1_4, t_tfinger_3_1_4, t_tseen_2_1_4, t_tseen_3_1_4 \ t_tfinger_2_5, t_tfinger_3_5, t_tseen_
> 2_5, t_tseen_3_5 \ t_tfinger_2_6, t_tfinger_3_6, t_tseen_2_6, t_tseen_3_6 \ t_tfinger_2_7, t_t
> finger_3_7, t_tseen_2_7, t_tseen_3_7)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_turnoutcarta.xml") append sheet("
> turnout 2") 


note: results saved to outputregs_turnoutcarta.xml

. xml_tab $list2, save("outputregs_turnoutcarta.xml") append sheet("turnout 2 stats") 


note: results saved to outputregs_turnoutcarta.xml

. estimates clear

. 
. global turnout2="tseen"

. 
. global list1=""

. global list2=""

. 
. foreach i in $turnout2 {
  2. 
.         xi: regress `i' $treat $prov i.dayselec if time==1, cluster(ea)
  3.         estimates store `i'_0_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_0_1=r(mean)
  6.         display m_`i'_0_1
  7.         test civiceduc = hotline
  8.         scalar define t_`i'_0_1_1=r(p)
  9.         display t_`i'_0_1_1
 10.         test civiceduc = verdade
 11.         scalar define t_`i'_0_1_2=r(p)
 12.         display t_`i'_0_1_2
 13.         test hotline = verdade
 14.         scalar define t_`i'_0_1_3=r(p)
 15.         display t_`i'_0_1_3
 16.         test civiceduc hotline verdade
 17.         scalar define t_`i'_0_1_4=r(p)
 18.         display t_`i'_0_1_4
 19. 
.         regress `i' $treat $prov $ea $controls i.dayselec if time==1, cluster(ea)
 20.         estimates store `i'_0_2
 21.         sum `i' if e(sample) & control == 1
 22.         scalar define m_`i'_0_2=r(mean)
 23.         display m_`i'_0_2
 24.         test civiceduc = hotline
 25.         scalar define t_`i'_0_2_1=r(p)
 26.         display t_`i'_0_2_1
 27.         test civiceduc = verdade
 28.         scalar define t_`i'_0_2_2=r(p)
 29.         display t_`i'_0_2_2
 30.         test hotline = verdade
 31.         scalar define t_`i'_0_2_3=r(p)
 32.         display t_`i'_0_2_3
 33.         test civiceduc hotline verdade
 34.         scalar define t_`i'_0_2_4=r(p)
 35.         display t_`i'_0_2_4
 36. 
.         regress `i' $treat $prov if time==1 & pt_dayselec>0 & pt_dayselec<50, cluster(ea)
 37.         estimates store `i'_1_1
 38.         sum `i' if e(sample) & control == 1
 39.         scalar define m_`i'_1_1=r(mean)
 40.         display m_`i'_1_1
 41.         test civiceduc = hotline
 42.         scalar define t_`i'_1_1_1=r(p)
 43.         display t_`i'_1_1_1
 44.         test civiceduc = verdade
 45.         scalar define t_`i'_1_1_2=r(p)
 46.         display t_`i'_1_1_2
 47.         test hotline = verdade
 48.         scalar define t_`i'_1_1_3=r(p)
 49.         display t_`i'_1_1_3
 50.         test civiceduc hotline verdade
 51.         scalar define t_`i'_1_1_4=r(p)
 52.         display t_`i'_1_1_4
 53. 
.         regress `i' $treat $prov $ea $controls if time==1 & pt_dayselec>0 & pt_dayselec<50, cl
> uster(ea)
 54.         estimates store `i'_1_2
 55.         sum `i' if e(sample) & control == 1
 56.         scalar define m_`i'_1_2=r(mean)
 57.         display m_`i'_1_2
 58.         test civiceduc = hotline
 59.         scalar define t_`i'_1_2_1=r(p)
 60.         display t_`i'_1_2_1
 61.         test civiceduc = verdade
 62.         scalar define t_`i'_1_2_2=r(p)
 63.         display t_`i'_1_2_2
 64.         test hotline = verdade
 65.         scalar define t_`i'_1_2_3=r(p)
 66.         display t_`i'_1_2_3
 67.         test civiceduc hotline verdade
 68.         scalar define t_`i'_1_2_4=r(p)
 69.         display t_`i'_1_2_4
 70.         
.         regress `i' $treat $prov if time==1 & pt_dayselec>=50 & pt_dayselec<=100, cluster(ea)
 71.         estimates store `i'_2_1
 72.         sum `i' if e(sample) & control == 1
 73.         scalar define m_`i'_2_1=r(mean)
 74.         display m_`i'_2_1
 75.         test civiceduc = hotline
 76.         scalar define t_`i'_2_1_1=r(p)
 77.         display t_`i'_2_1_1
 78.         test civiceduc = verdade
 79.         scalar define t_`i'_2_1_2=r(p)
 80.         display t_`i'_2_1_2
 81.         test hotline = verdade
 82.         scalar define t_`i'_2_1_3=r(p)
 83.         display t_`i'_2_1_3
 84.         test civiceduc hotline verdade
 85.         scalar define t_`i'_2_1_4=r(p)
 86.         display t_`i'_2_1_4
 87. 
.         regress `i' $treat $prov $ea $controls if time==1 & pt_dayselec>=50 & pt_dayselec<=100
> , cluster(ea)
 88.         estimates store `i'_2_2
 89.         sum `i' if e(sample) & control == 1
 90.         scalar define m_`i'_2_2=r(mean)
 91.         display m_`i'_2_2
 92.         test civiceduc = hotline
 93.         scalar define t_`i'_2_2_1=r(p)
 94.         display t_`i'_2_2_1
 95.         test civiceduc = verdade
 96.         scalar define t_`i'_2_2_2=r(p)
 97.         display t_`i'_2_2_2
 98.         test hotline = verdade
 99.         scalar define t_`i'_2_2_3=r(p)
100.         display t_`i'_2_2_3
101.         test civiceduc hotline verdade
102.         scalar define t_`i'_2_2_4=r(p)
103.         display t_`i'_2_2_4
104. 
.         global list1="$list1" + " `i'_0_1" + " `i'_0_2" + " `i'_1_1" + " `i'_1_2" + " `i'_2_1"
>  + " `i'_2_2"     
105.         }
i.dayselecpost    _Idayselecp_7-34    (naturally coded; _Idayselecp_7 omitted)

Linear regression                                      Number of obs =    1100
                                                       F( 28,   160) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.0794
                                                       Root MSE      =  .44287

                                     (Std. Err. adjusted for 161 clusters in ea)
--------------------------------------------------------------------------------
               |               Robust
         tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |   .0494553   .0310538     1.59   0.113    -.0118729    .1107835
       hotline |   .0662635   .0372865     1.78   0.077    -.0073737    .1399007
       verdade |   .0873705   .0375815     2.32   0.021     .0131507    .1615902
           pr1 |   .1161925    .031904     3.64   0.000     .0531853    .1791997
           pr2 |   .1519656   .0368416     4.12   0.000      .079207    .2247242
           pr3 |   .2305215   .0365475     6.31   0.000     .1583437    .3026992
 _Idayselecp_8 |   .0778538   .0806623     0.97   0.336    -.0814464     .237154
 _Idayselecp_9 |  -.1713678   .0677089    -2.53   0.012    -.3050862   -.0376493
_Idayselecp_10 |  -.1906671   .0713264    -2.67   0.008    -.3315298   -.0498044
_Idayselecp_11 |   -.136425   .0580503    -2.35   0.020    -.2510686   -.0217814
_Idayselecp_12 |  -.0761372   .0837286    -0.91   0.365    -.2414928    .0892185
_Idayselecp_13 |  -.0512418   .0690778    -0.74   0.459    -.1876636    .0851799
_Idayselecp_14 |  -.0679904    .061119    -1.11   0.268    -.1886944    .0527135
_Idayselecp_15 |   -.226251    .053128    -4.26   0.000    -.3311735   -.1213285
_Idayselecp_16 |   -.159578   .0587278    -2.72   0.007    -.2755597   -.0435964
_Idayselecp_17 |  -.0885581   .0475244    -1.86   0.064    -.1824142     .005298
_Idayselecp_18 |  -.1982801   .0464825    -4.27   0.000    -.2900786   -.1064817
_Idayselecp_19 |  -.2749603   .0605501    -4.54   0.000    -.3945408   -.1553798
_Idayselecp_20 |  -.1339163   .0619071    -2.16   0.032    -.2561768   -.0116558
_Idayselecp_21 |  -.2039178   .0631849    -3.23   0.002    -.3287017   -.0791339
_Idayselecp_22 |  -.2682092   .0583486    -4.60   0.000     -.383442   -.1529765
_Idayselecp_23 |  -.2627249   .0402182    -6.53   0.000    -.3421519   -.1832979
_Idayselecp_24 |  -.2784983   .0575637    -4.84   0.000     -.392181   -.1648156
_Idayselecp_25 |  -.1955466   .0536742    -3.64   0.000    -.3015478   -.0895454
_Idayselecp_26 |  -.2570292   .0607099    -4.23   0.000    -.3769252   -.1371332
_Idayselecp_27 |  -.2785913   .0519875    -5.36   0.000    -.3812614   -.1759212
_Idayselecp_28 |  -.2343503   .0828817    -2.83   0.005    -.3980336   -.0706671
_Idayselecp_29 |  -.2568684   .1048195    -2.45   0.015    -.4638766   -.0498602
_Idayselecp_30 |  -.1535168    .080808    -1.90   0.059    -.3131046     .006071
_Idayselecp_34 |  -.3480344   .0368416    -9.45   0.000     -.420793   -.2752758
         _cons |   .2985791   .0468093     6.38   0.000     .2061354    .3910229
--------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tseen |       262    .2442748    .4304787          0          1
.24427481

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.23
            Prob > F =    0.6335
.63348048

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    1.18
            Prob > F =    0.2780
.2780386

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.28
            Prob > F =    0.5985
.59850586

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    2.07
            Prob > F =    0.1069
.10687183

Linear regression                                      Number of obs =    1086
                                                       F( 47,   160) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.1056
                                                       Root MSE      =  .44183

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .050831   .0340332     1.49   0.137    -.0163812    .1180432
     hotline |   .0616897   .0371628     1.66   0.099    -.0117031    .1350825
     verdade |   .0948573   .0406695     2.33   0.021     .0145391    .1751755
         pr1 |   .1178567   .0405113     2.91   0.004     .0378508    .1978626
         pr2 |   .1316327   .0452983     2.91   0.004      .042173    .2210924
         pr3 |   .2095855    .042982     4.88   0.000     .1247003    .2944707
        post |  -.0013966   .0495181    -0.03   0.978      -.09919    .0963968
   post_miss |  -.1075094   .0528025    -2.04   0.043    -.2117891   -.0032297
      health |   .0301802   .0331616     0.91   0.364    -.0353107    .0956711
 health_miss |   .1049437   .0487312     2.15   0.033     .0087044     .201183
         sex |   .0404876   .0313979     1.29   0.199    -.0215202    .1024955
         age |   .0036222   .0011819     3.06   0.003      .001288    .0059564
      single |  -.0513201   .0325833    -1.58   0.117    -.1156689    .0130287
       divor |  -.1792231    .125607    -1.43   0.156    -.4272847    .0688384
     protest |   .0128762   .0332161     0.39   0.699    -.0527223    .0784747
         com |  -.0046293   .0709584    -0.07   0.948    -.1447651    .1355065
        prof |     .20299   .1453762     1.40   0.165    -.0841136    .4900936
         tea |   .0088234   .0672876     0.13   0.896    -.1240631    .1417098
     comform |  -.0086515   .1003489    -0.09   0.931    -.2068308    .1895277
         dom |    .018068   .0387564     0.47   0.642    -.0584721     .094608
    econfood |  -.0015893   .0136287    -0.12   0.907    -.0285046    .0253259
       house |   .0181274   .0386772     0.47   0.640    -.0582563    .0945111
      llomue |     .01659   .0639123     0.26   0.796    -.1096304    .1428104
     chitsua |  -.0693119   .1128336    -0.61   0.540    -.2921471    .1535233
      living |   .0077046   .0120979     0.64   0.525    -.0161875    .0315967
             |
dayselecpost |
          8  |   .0988789    .101099     0.98   0.330    -.1007818    .2985395
          9  |  -.1870781   .0769422    -2.43   0.016    -.3390314   -.0351247
         10  |  -.1773809   .0763877    -2.32   0.021    -.3282392   -.0265227
         11  |  -.1176095   .0549341    -2.14   0.034    -.2260989   -.0091201
         12  |  -.0724645   .0832332    -0.87   0.385    -.2368419    .0919129
         13  |  -.0625078   .0782498    -0.80   0.426    -.2170435    .0920278
         14  |  -.0816084   .0650702    -1.25   0.212    -.2101156    .0468988
         15  |  -.2105381   .0610461    -3.45   0.001    -.3310981   -.0899781
         16  |  -.1449038   .0627575    -2.31   0.022    -.2688437   -.0209639
         17  |  -.0975994   .0540845    -1.80   0.073     -.204411    .0092121
         18  |  -.1952245   .0556043    -3.51   0.001    -.3050374   -.0854115
         19  |  -.2755145   .0659533    -4.18   0.000    -.4057657   -.1452633
         20  |  -.1378873   .0650367    -2.12   0.036    -.2663283   -.0094463
         21  |  -.1884034   .0729447    -2.58   0.011     -.332462   -.0443449
         22  |  -.2568847   .0634368    -4.05   0.000    -.3821661   -.1316032
         23  |  -.2513772   .0447519    -5.62   0.000    -.3397579   -.1629966
         24  |  -.2697264   .0630824    -4.28   0.000    -.3943079   -.1451449
         25  |   -.177731   .0557538    -3.19   0.002    -.2878394   -.0676227
         26  |  -.2476889   .0673965    -3.68   0.000    -.3807903   -.1145875
         27  |  -.2813205   .0564385    -4.98   0.000     -.392781   -.1698599
         28  |  -.2179816   .0882014    -2.47   0.015    -.3921707   -.0437925
         29  |  -.2541119   .1109342    -2.29   0.023     -.473196   -.0350278
         30  |  -.1399849   .0765049    -1.83   0.069    -.2910745    .0111046
         34  |  -.3569989   .0442381    -8.07   0.000    -.4443647   -.2696331
             |
       _cons |   .0935967   .0872076     1.07   0.285    -.0786296     .265823
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tseen |       260    .2461538       .4316          0          1
.24615385

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.09
            Prob > F =    0.7640
.76399648

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    1.49
            Prob > F =    0.2235
.22353541

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.64
            Prob > F =    0.4234
.42338872

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.98
            Prob > F =    0.1190
.11903126

Linear regression                                      Number of obs =     546
                                                       F(  6,    85) =    3.72
                                                       Prob > F      =  0.0025
                                                       R-squared     =  0.0394
                                                       Root MSE      =   .4732

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0471478   .0468566     1.01   0.317    -.0460156    .1403113
     hotline |   .0987414   .0481365     2.05   0.043     .0030332    .1944497
     verdade |    .093743     .05435     1.72   0.088    -.0143194    .2018055
         pr1 |   .0971015   .0503839     1.93   0.057    -.0030752    .1972782
         pr2 |   .1818619   .0555975     3.27   0.002     .0713191    .2924047
         pr3 |   .2393231   .0645266     3.71   0.000     .1110269    .3676192
       _cons |   .1607302   .0475453     3.38   0.001     .0661974     .255263
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tseen |       131    .2977099    .4590066          0          1
.29770992

 ( 1)  civiceduc - hotline = 0

       F(  1,    85) =    0.87
            Prob > F =    0.3539
.3539235

 ( 1)  civiceduc - verdade = 0

       F(  1,    85) =    0.60
            Prob > F =    0.4422
.44218027

 ( 1)  hotline - verdade = 0

       F(  1,    85) =    0.01
            Prob > F =    0.9354
.93536749

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    1.88
            Prob > F =    0.1396
.13961511

Linear regression                                      Number of obs =     541
                                                       F( 25,    85) =    7.47
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0853
                                                       Root MSE      =  .47065

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .039976   .0617102     0.65   0.519    -.0827204    .1626724
     hotline |    .106485    .053204     2.00   0.049     .0007013    .2122688
     verdade |    .115367   .0656913     1.76   0.083     -.015245    .2459789
         pr1 |   .0787611   .0560529     1.41   0.164     -.032687    .1902093
         pr2 |   .1736152   .0670221     2.59   0.011     .0403573    .3068732
         pr3 |   .2183691   .0718348     3.04   0.003     .0755422     .361196
        post |  -.0369648   .0670932    -0.55   0.583    -.1703642    .0964345
   post_miss |  -.0286082   .0576309    -0.50   0.621    -.1431938    .0859774
      health |   .0800724   .0527399     1.52   0.133    -.0247887    .1849336
 health_miss |   .0475611   .0659489     0.72   0.473     -.083563    .1786853
         sex |   .0306449   .0519769     0.59   0.557    -.0726992    .1339889
         age |   .0034576    .001669     2.07   0.041     .0001392     .006776
      single |  -.0426874   .0476807    -0.90   0.373    -.1374895    .0521147
       divor |  -.4416398    .064644    -6.83   0.000    -.5701694   -.3131101
     protest |   .0233916   .0501691     0.47   0.642    -.0763581    .1231412
         com |   .0348085   .1362615     0.26   0.799    -.2361158    .3057328
        prof |   .2428421   .1599066     1.52   0.133    -.0750951    .5607793
         tea |   .0843816   .0964487     0.87   0.384    -.1073841    .2761474
     comform |   .4623048   .1905137     2.43   0.017     .0835124    .8410971
         dom |   .0373308     .05749     0.65   0.518    -.0769748    .1516363
    econfood |   .0123409   .0218022     0.57   0.573    -.0310077    .0556895
       house |   .0089563   .0597284     0.15   0.881    -.1097998    .1277124
      llomue |   .0816477   .0814462     1.00   0.319    -.0802891    .2435845
     chitsua |  -.2005411   .1717842    -1.17   0.246     -.542094    .1410119
      living |  -.0035133   .0190385    -0.18   0.854    -.0413668    .0343403
       _cons |  -.0523534   .1434605    -0.36   0.716    -.3375912    .2328845
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tseen |       131    .2977099    .4590066          0          1
.29770992

 ( 1)  civiceduc - hotline = 0

       F(  1,    85) =    1.40
            Prob > F =    0.2408
.24076951

 ( 1)  civiceduc - verdade = 0

       F(  1,    85) =    1.51
            Prob > F =    0.2228
.22275283

 ( 1)  hotline - verdade = 0

       F(  1,    85) =    0.02
            Prob > F =    0.8831
.88312538

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    1.83
            Prob > F =    0.1470
.1469896

Linear regression                                      Number of obs =     554
                                                       F(  6,    93) =    7.98
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0479
                                                       Root MSE      =  .41162

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0640405   .0384633     1.66   0.099    -.0123401     .140421
     hotline |   .0309352   .0517596     0.60   0.552     -.071849    .1337194
     verdade |    .062672   .0520943     1.20   0.232    -.0407768    .1661209
         pr1 |   .1353749    .042964     3.15   0.002      .050057    .2206928
         pr2 |   .1283171   .0528057     2.43   0.017     .0234553    .2331788
         pr3 |   .2398829    .036628     6.55   0.000      .167147    .3126188
       _cons |   .0677245    .038514     1.76   0.082    -.0087567    .1442057
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tseen |       131    .1908397    .3944715          0          1
.19083969

 ( 1)  civiceduc - hotline = 0

       F(  1,    93) =    0.51
            Prob > F =    0.4786
.47863453

 ( 1)  civiceduc - verdade = 0

       F(  1,    93) =    0.00
            Prob > F =    0.9766
.97663696

 ( 1)  hotline - verdade = 0

       F(  1,    93) =    0.29
            Prob > F =    0.5887
.58865995

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    1.02
            Prob > F =    0.3868
.38683605

Linear regression                                      Number of obs =     545
                                                       F( 25,    93) =   23.13
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1094
                                                       Root MSE      =  .40772

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0524891   .0351408     1.49   0.139    -.0172936    .1222718
     hotline |   -.031073   .0460212    -0.68   0.501    -.1224618    .0603159
     verdade |   .0573521   .0498131     1.15   0.253    -.0415668    .1562711
         pr1 |   .2804286    .055152     5.08   0.000     .1709076    .3899495
         pr2 |     .12329   .0580678     2.12   0.036     .0079789    .2386011
         pr3 |   .2262091   .0476786     4.74   0.000     .1315288    .3208893
        post |  -.0191436   .0504415    -0.38   0.705    -.1193105    .0810232
   post_miss |  -.1915609   .0539633    -3.55   0.001    -.2987213   -.0844005
      health |   .0040423   .0435865     0.09   0.926    -.0825119    .0905966
 health_miss |   .0732029   .0652275     1.12   0.265    -.0563259    .2027318
         sex |   .0699366   .0370759     1.89   0.062    -.0036888     .143562
         age |   .0045739   .0016317     2.80   0.006     .0013337    .0078141
      single |  -.0119397   .0414111    -0.29   0.774    -.0941739    .0702944
       divor |   .1229508   .2116381     0.58   0.563    -.2973205    .5432221
     protest |   .0195658   .0437849     0.45   0.656    -.0673823    .1065139
         com |  -.0123855   .0769277    -0.16   0.872    -.1651487    .1403777
        prof |   .2816783   .2924218     0.96   0.338    -.2990135    .8623701
         tea |  -.1075257   .0830347    -1.29   0.199    -.2724162    .0573647
     comform |  -.2332817   .0565744    -4.12   0.000    -.3456272   -.1209362
         dom |  -.0256883   .0526166    -0.49   0.627    -.1301745    .0787979
    econfood |  -.0165184   .0179119    -0.92   0.359    -.0520878     .019051
       house |   .0649113   .0529902     1.22   0.224    -.0403167    .1701393
      llomue |  -.1314438   .0901141    -1.46   0.148    -.3103925    .0475049
     chitsua |   .0908453   .1506246     0.60   0.548    -.2082653    .3899559
      living |   .0187963   .0149906     1.25   0.213     -.010972    .0485646
       _cons |  -.2159201   .0814715    -2.65   0.009    -.3777064   -.0541338
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tseen |       129    .1937984    .3968139          0          1
.19379845

 ( 1)  civiceduc - hotline = 0

       F(  1,    93) =    3.23
            Prob > F =    0.0757
.07571547

 ( 1)  civiceduc - verdade = 0

       F(  1,    93) =    0.01
            Prob > F =    0.9142
.91422348

 ( 1)  hotline - verdade = 0

       F(  1,    93) =    2.18
            Prob > F =    0.1427
.14274695

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    1.43
            Prob > F =    0.2397
.23965601

. 
. matrix define means=(m_tseen_0_1, m_tseen_0_2, m_tseen_1_1, m_tseen_1_2, m_tseen_2_1, m_tseen_
> 2_2 \ t_tseen_0_1_1, t_tseen_0_2_1, t_tseen_1_1_1, t_tseen_1_2_1, t_tseen_2_1_1, t_tseen_2_2_1
>  \ t_tseen_0_1_2, t_tseen_0_2_2, t_tseen_1_1_2, t_tseen_1_2_2, t_tseen_2_1_2, t_tseen_2_2_2 \ 
> t_tseen_0_1_3, t_tseen_0_2_3, t_tseen_1_1_3, t_tseen_1_2_3, t_tseen_2_1_3, t_tseen_2_2_3 \ t_t
> seen_0_1_4, t_tseen_0_2_4, t_tseen_1_1_4, t_tseen_1_2_4, t_tseen_2_1_4, t_tseen_2_2_4)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_turnoutcarta.xml") append sheet("
> turnout 2 rob 1") 


note: results saved to outputregs_turnoutcarta.xml

. xml_tab $list2, save("outputregs_turnoutcarta.xml") append sheet("turnout 2 rob 1 stats") 


note: results saved to outputregs_turnoutcarta.xml

. estimates clear

. 
. **********************************************************************************************
> *********
. *****  OA TABLES 9: BALANCE FOR SURVEY HALVES (ROBUSTNESS FOR REGRESSIONS OF INDIVIDUAL TURNOU
> T)  *****
. **********************************************************************************************
> *********
. 
. sort time obsid

. forvalues i=1(1)1766 {
  2. replace pt_dayselec=pt_dayselec[1766+`i'] in `i' 
  3. }
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. 
. global demo1="sex age head housen single marriedunion noschl informalschl lit prim5y sec10y"

. global demo2="chang macua lomue chuabo chironga maconde"

. global demo3="cathol protest muslim"

. global demo4="job agric com art man assal tea puboff stud dom"

. global demo5="house land cattle cel expenditure"

. global demo6="netmean_dist"

. 
. *half1
. 
. foreach i in $demo1 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & pt_dayselec>0 & pt_daysel
> ec<50, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & pt_dayselec>0 & pt_day
> selec<50, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & pt_dayselec>0 & pt_day
> selec<50, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0 & pt_dayselec>0 & pt_dayselec<50, 
> cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 14.         xml_tab $list1, below save(balance_halves.xml) append sheet("bal `i' 1")
 15.         estimates clear
 16. 
. }

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    0.24
                                                       Prob > F      =  0.6279
                                                       R-squared     =  0.0011
                                                       Root MSE      =  .49202

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         sex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0326645   .0668597    -0.49   0.628     -.167901    .1025719
       _cons |   .4179104   .0430959     9.70   0.000     .3307408    .5050801
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.02
                                                       Prob > F      =  0.8915
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .49406

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         sex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0082719   .0603193    -0.14   0.892    -.1297612    .1132174
       _cons |   .4179104   .0430115     9.72   0.000     .3312808    .5045401
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     279
                                                       F(  1,    43) =    0.23
                                                       Prob > F      =  0.6359
                                                       R-squared     =  0.0009
                                                       Root MSE      =  .49714

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         sex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0303654   .0636809     0.48   0.636    -.0980594    .1587902
       _cons |   .4179104   .0430387     9.71   0.000     .3311146    .5047063
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  3,    85) =    0.29
                                                       Prob > F      =  0.8328
                                                       R-squared     =  0.0020
                                                       Root MSE      =  .49419

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         sex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0326645   .0664519    -0.49   0.624    -.1647886    .0994596
     hotline |  -.0082719    .060069    -0.14   0.891    -.1277051    .1111613
     verdade |   .0303654   .0633765     0.48   0.633    -.0956441    .1563749
       _cons |   .4179104    .042833     9.76   0.000      .332747    .5030739
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.29
            Prob > F =    0.8328
.83283882


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     255
                                                       F(  1,    39) =    0.13
                                                       Prob > F      =  0.7204
                                                       R-squared     =  0.0005
                                                       Root MSE      =  13.513

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .6155175   1.707127     0.36   0.720    -2.837472    4.068507
       _cons |   37.80597   1.189701    31.78   0.000     35.39957    40.21237
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.13
                                                       Prob > F      =  0.7197
                                                       R-squared     =  0.0005
                                                       Root MSE      =  12.685

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.5710304    1.58145    -0.36   0.720    -3.756234    2.614173
       _cons |   37.80597   1.187363    31.84   0.000      35.4145    40.19744
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    0.02
                                                       Prob > F      =  0.8974
                                                       R-squared     =  0.0001
                                                       Root MSE      =   13.32

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.2469424   1.904253    -0.13   0.897    -4.087234    3.593349
       _cons |   37.80597   1.188122    31.82   0.000     35.40989    40.20205
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  3,    85) =    0.19
                                                       Prob > F      =  0.9037
                                                       R-squared     =  0.0010
                                                       Root MSE      =   13.43

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .6155175   1.696717     0.36   0.718     -2.75801    3.989045
     hotline |  -.5710304   1.574901    -0.36   0.718    -3.702355    2.560294
     verdade |  -.2469424   1.895156    -0.13   0.897     -4.01502    3.521136
       _cons |   37.80597   1.182446    31.97   0.000     35.45495    40.15699
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.19
            Prob > F =    0.9037
.90366725


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    0.02
                                                       Prob > F      =  0.8889
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .45134

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        head |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0107658   .0765667    -0.14   0.889    -.1656366    .1441049
       _cons |   .7238806   .0636491    11.37   0.000     .5951381    .8526231
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.01
                                                       Prob > F      =  0.9201
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .45049

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        head |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0070131   .0694976    -0.10   0.920    -.1469885    .1329622
       _cons |   .7238806   .0635245    11.40   0.000     .5959356    .8518256
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     279
                                                       F(  1,    43) =    0.23
                                                       Prob > F      =  0.6373
                                                       R-squared     =  0.0016
                                                       Root MSE      =   .4388

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        head |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0347401   .0731526     0.47   0.637    -.1127863    .1822665
       _cons |   .7238806   .0635647    11.39   0.000     .5956901    .8520711
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  3,    85) =    0.34
                                                       Prob > F      =  0.7994
                                                       R-squared     =  0.0017
                                                       Root MSE      =  .44599

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        head |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0107658   .0760996    -0.14   0.888    -.1620723    .1405406
     hotline |  -.0070131   .0692091    -0.10   0.920    -.1446194    .1305932
     verdade |   .0347401    .072803     0.48   0.634    -.1100118    .1794919
       _cons |   .7238806   .0632609    11.44   0.000      .598101    .8496602
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.34
            Prob > F =    0.7994
.79944591


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    0.02
                                                       Prob > F      =  0.8805
                                                       R-squared     =  0.0001
                                                       Root MSE      =  2.8285

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      housen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0647786   .4280139    -0.15   0.880    -.9305183    .8009612
       _cons |   6.216418   .1877635    33.11   0.000      5.83663    6.596205
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.07
                                                       Prob > F      =  0.7987
                                                       R-squared     =  0.0002
                                                       Root MSE      =  2.5124

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      housen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0778637   .3034696    -0.26   0.799    -.6890829    .5333555
       _cons |   6.216418    .187396    33.17   0.000     5.838983    6.593853
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     279
                                                       F(  1,    43) =    0.07
                                                       Prob > F      =  0.7893
                                                       R-squared     =  0.0002
                                                       Root MSE      =  2.9523

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      housen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0870304    .323593     0.27   0.789     -.565557    .7396178
       _cons |   6.216418   .1875145    33.15   0.000     5.838259    6.594577
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  3,    85) =    0.08
                                                       Prob > F      =  0.9705
                                                       R-squared     =  0.0005
                                                       Root MSE      =  2.9405

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      housen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0647786   .4254031    -0.15   0.879     -.910594    .7810368
     hotline |  -.0778637     .30221    -0.26   0.797    -.6787382    .5230108
     verdade |   .0870304   .3220462     0.27   0.788    -.5532838    .7273446
       _cons |   6.216418   .1866182    33.31   0.000     5.845371    6.587465
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.08
            Prob > F =    0.9705
.97046395


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    0.81
                                                       Prob > F      =  0.3726
                                                       R-squared     =  0.0033
                                                       Root MSE      =   .3912

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      single |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0450208   .0499081    -0.90   0.373    -.1459694    .0559278
       _cons |   .2089552   .0400224     5.22   0.000     .1280023    .2899081
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.01
                                                       Prob > F      =  0.9330
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .40627

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      single |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0041359   .0488986    -0.08   0.933    -.1026228    .0943509
       _cons |   .2089552    .039944     5.23   0.000     .1285038    .2894066
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     279
                                                       F(  1,    43) =    0.45
                                                       Prob > F      =  0.5065
                                                       R-squared     =  0.0022
                                                       Root MSE      =  .39326

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      single |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0365414   .0545515    -0.67   0.507    -.1465551    .0734722
       _cons |   .2089552   .0399693     5.23   0.000     .1283495     .289561
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  3,    85) =    0.49
                                                       Prob > F      =  0.6905
                                                       R-squared     =  0.0024
                                                       Root MSE      =  .39219

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      single |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0450208   .0496036    -0.91   0.367    -.1436461    .0536045
     hotline |  -.0041359   .0486956    -0.08   0.933    -.1009559     .092684
     verdade |  -.0365414   .0542908    -0.67   0.503     -.144486    .0714032
       _cons |   .2089552   .0397782     5.25   0.000     .1298654     .288045
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.49
            Prob > F =    0.6905
.69045229


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    0.48
                                                       Prob > F      =  0.4907
                                                       R-squared     =  0.0027
                                                       Root MSE      =  .46781

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
marriedunion |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0482016   .0692712     0.70   0.491    -.0919125    .1883158
       _cons |   .6567164   .0433939    15.13   0.000     .5689439    .7444889
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.85
                                                       Prob > F      =  0.3627
                                                       R-squared     =  0.0034
                                                       Root MSE      =  .46462

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
marriedunion |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |    .054127    .058859     0.92   0.363    -.0644212    .1726751
       _cons |   .6567164    .043309    15.16   0.000     .5694876    .7439452
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     279
                                                       F(  1,    43) =    1.50
                                                       Prob > F      =  0.2270
                                                       R-squared     =  0.0065
                                                       Root MSE      =  .46042

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
marriedunion |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0743181   .0606367     1.23   0.227    -.0479676    .1966037
       _cons |   .6567164   .0433364    15.15   0.000     .5693203    .7441126
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  3,    85) =    0.54
                                                       Prob > F      =  0.6546
                                                       R-squared     =  0.0035
                                                       Root MSE      =  .45823

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
marriedunion |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0482016   .0688486     0.70   0.486    -.0886879    .1850911
     hotline |    .054127   .0586147     0.92   0.358    -.0624148    .1706687
     verdade |   .0743181   .0603469     1.23   0.222    -.0456678    .1943039
       _cons |   .6567164   .0431292    15.23   0.000     .5709639    .7424689
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.54
            Prob > F =    0.6546
.65458658


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    4.56
                                                       Prob > F      =  0.0390
                                                       R-squared     =  0.0241
                                                       Root MSE      =   .3775

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      noschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1183019   .0553886     2.14   0.039     .0062679     .230336
       _cons |    .119403    .032981     3.62   0.001     .0526926    .1861133
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    1.29
                                                       Prob > F      =  0.2613
                                                       R-squared     =  0.0059
                                                       Root MSE      =   .3572

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      noschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0552958   .0486017     1.14   0.261    -.0425931    .1531847
       _cons |    .119403   .0329164     3.63   0.001     .0531059    .1857001
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    0.20
                                                       Prob > F      =  0.6541
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .33682

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      noschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0194859   .0431907     0.45   0.654    -.0676165    .1065883
       _cons |    .119403   .0329375     3.63   0.001     .0529783    .1858277
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    85) =    1.77
                                                       Prob > F      =  0.1588
                                                       R-squared     =  0.0132
                                                       Root MSE      =  .37099

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      noschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1183019    .055051     2.15   0.034     .0088458    .2277581
     hotline |   .0552958   .0484002     1.14   0.256    -.0409368    .1515285
     verdade |   .0194859   .0429842     0.45   0.651    -.0659782      .10495
       _cons |    .119403     .03278     3.64   0.000     .0542276    .1845783
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    1.77
            Prob > F =    0.1588
.1587964


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    0.03
                                                       Prob > F      =  0.8614
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .24299

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
informalschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0058723   .0334147     0.18   0.861    -.0617153    .0734599
       _cons |   .0597015   .0202947     2.94   0.005     .0186516    .1007514
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.04
                                                       Prob > F      =  0.8425
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .23196

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
informalschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0054846   .0274359    -0.20   0.842    -.0607433    .0497741
       _cons |   .0597015    .020255     2.95   0.005     .0189059    .1004971
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    0.52
                                                       Prob > F      =  0.4733
                                                       R-squared     =  0.0017
                                                       Root MSE      =  .21929

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
informalschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0180348   .0249288    -0.72   0.473    -.0683085    .0322388
       _cons |   .0597015   .0202679     2.95   0.005     .0188274    .1005756
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    85) =    0.31
                                                       Prob > F      =  0.8169
                                                       R-squared     =  0.0014
                                                       Root MSE      =  .22817

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
informalschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0058723    .033211     0.18   0.860    -.0601602    .0719047
     hotline |  -.0054846   .0273221    -0.20   0.841    -.0598083    .0488391
     verdade |  -.0180348   .0248096    -0.73   0.469    -.0673629    .0312933
       _cons |   .0597015    .020171     2.96   0.004     .0195962    .0998068
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.31
            Prob > F =    0.8169
.81691734


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    4.56
                                                       Prob > F      =  0.0390
                                                       R-squared     =  0.0241
                                                       Root MSE      =   .3775

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         lit |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1183019   .0553886    -2.14   0.039     -.230336   -.0062679
       _cons |    .880597    .032981    26.70   0.000     .8138867    .9473074
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    1.29
                                                       Prob > F      =  0.2613
                                                       R-squared     =  0.0059
                                                       Root MSE      =   .3572

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         lit |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0552958   .0486017    -1.14   0.261    -.1531847    .0425931
       _cons |    .880597   .0329164    26.75   0.000     .8142999    .9468941
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    0.20
                                                       Prob > F      =  0.6541
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .33682

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         lit |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0194859   .0431907    -0.45   0.654    -.1065883    .0676165
       _cons |    .880597   .0329375    26.74   0.000     .8141723    .9470217
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    85) =    1.77
                                                       Prob > F      =  0.1588
                                                       R-squared     =  0.0132
                                                       Root MSE      =  .37099

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         lit |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1183019    .055051    -2.15   0.034    -.2277581   -.0088458
     hotline |  -.0552958   .0484002    -1.14   0.256    -.1515285    .0409368
     verdade |  -.0194859   .0429842    -0.45   0.651      -.10495    .0659782
       _cons |    .880597     .03278    26.86   0.000     .8154217    .9457724
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    1.77
            Prob > F =    0.1588
.1587964


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    0.16
                                                       Prob > F      =  0.6878
                                                       R-squared     =  0.0006
                                                       Root MSE      =  .44326

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      prim5y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.022021   .0543992    -0.40   0.688    -.1320539    .0880118
       _cons |   .2761194   .0378103     7.30   0.000     .1996408     .352598
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.68
                                                       Prob > F      =  0.4137
                                                       R-squared     =  0.0022
                                                       Root MSE      =  .43589

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      prim5y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0411796   .0499127    -0.83   0.414    -.1417089    .0593496
       _cons |   .2761194   .0377363     7.32   0.000     .2001145    .3521243
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    1.16
                                                       Prob > F      =  0.2884
                                                       R-squared     =  0.0048
                                                       Root MSE      =  .46278

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      prim5y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0641584    .059692     1.07   0.288    -.0562221    .1845388
       _cons |   .2761194   .0377604     7.31   0.000     .1999682    .3522706
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    85) =    1.22
                                                       Prob > F      =  0.3057
                                                       R-squared     =  0.0083
                                                       Root MSE      =  .44656

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      prim5y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.022021   .0540677    -0.41   0.685     -.129522      .08548
     hotline |  -.0411796   .0497057    -0.83   0.410     -.140008    .0576487
     verdade |   .0641584   .0594066     1.08   0.283    -.0539579    .1822746
       _cons |   .2761194   .0375799     7.35   0.000     .2014005    .3508383
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    1.22
            Prob > F =    0.3057
.30569155


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    0.95
                                                       Prob > F      =  0.3368
                                                       R-squared     =  0.0032
                                                       Root MSE      =  .36019

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      sec10y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0404943   .0416359    -0.97   0.337    -.1247109    .0437224
       _cons |   .1716418   .0299926     5.72   0.000     .1109759    .2323076
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.05
                                                       Prob > F      =  0.8281
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .38264

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      sec10y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0090811   .0415755     0.22   0.828    -.0746562    .0928184
       _cons |   .1716418   .0299339     5.73   0.000     .1113517    .2319318
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    0.15
                                                       Prob > F      =  0.7039
                                                       R-squared     =  0.0007
                                                       Root MSE      =  .36954

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      sec10y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   -.018864   .0493018    -0.38   0.704    -.1182905    .0805625
       _cons |   .1716418   .0299531     5.73   0.000     .1112357    .2320479
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    85) =    0.57
                                                       Prob > F      =  0.6348
                                                       R-squared     =  0.0026
                                                       Root MSE      =  .36815

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      sec10y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0404943   .0413822    -0.98   0.331    -.1227731    .0417846
     hotline |   .0090811   .0414031     0.22   0.827    -.0732394    .0914016
     verdade |   -.018864    .049066    -0.38   0.702    -.1164204    .0786924
       _cons |   .1716418   .0298098     5.76   0.000     .1123718    .2309117
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.57
            Prob > F =    0.6348
.63477957


note: results saved to balance_halves.xml

. 
. foreach i in $demo2 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & pt_dayselec>0 & pt_daysel
> ec<50, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & pt_dayselec>0 & pt_day
> selec<50, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & pt_dayselec>0 & pt_day
> selec<50, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0 & pt_dayselec>0 & pt_dayselec<50, 
> cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 14.         xml_tab $list1, below save(balance_halves.xml) append sheet("bal `i' 1")
 15.         estimates clear
 16. 
. }

Linear regression                                      Number of obs =     255
                                                       F(  1,    39) =    0.33
                                                       Prob > F      =  0.5670
                                                       R-squared     =  0.0068
                                                       Root MSE      =  .49241

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       chang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0814111    .141018     0.58   0.567    -.2038246    .3666469
       _cons |   .3731343   .0864678     4.32   0.000     .1982367    .5480319
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.01
                                                       Prob > F      =  0.9245
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .48347

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       chang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0116885   .1226346    -0.10   0.924    -.2586872    .2353101
       _cons |   .3731343   .0862979     4.32   0.000     .1993215    .5469472
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    0.25
                                                       Prob > F      =  0.6218
                                                       R-squared     =  0.0043
                                                       Root MSE      =  .49189

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       chang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0643657   .1295303     0.50   0.622    -.1968571    .3255885
       _cons |   .3731343    .086353     4.32   0.000     .1989869    .5472818
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  3,    85) =    0.23
                                                       Prob > F      =  0.8753
                                                       R-squared     =  0.0066
                                                       Root MSE      =  .49072

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       chang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0814111    .140158     0.58   0.563    -.1972606    .3600829
     hotline |  -.0116885   .1221267    -0.10   0.924    -.2545092    .2311321
     verdade |   .0643657   .1289116     0.50   0.619    -.1919451    .3206764
       _cons |   .3731343   .0859405     4.34   0.000     .2022616    .5440071
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.23
            Prob > F =    0.8753
.87525544


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     255
                                                       F(  1,    39) =    0.00
                                                       Prob > F      =  0.9789
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .40157

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       macua |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0031454   .1181371    -0.03   0.979    -.2421003    .2358094
       _cons |   .2014925   .0745656     2.70   0.010     .0506694    .3523156
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.04
                                                       Prob > F      =  0.8453
                                                       R-squared     =  0.0007
                                                       Root MSE      =  .39348

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       macua |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0207696   .1058234    -0.20   0.845     -.233909    .1923697
       _cons |   .2014925   .0744191     2.71   0.010     .0516049    .3513802
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    0.43
                                                       Prob > F      =  0.5160
                                                       R-squared     =  0.0070
                                                       Root MSE      =  .37485

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       macua |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0626036    .095583    -0.65   0.516    -.2553651    .1301579
       _cons |   .2014925   .0744666     2.71   0.010     .0513163    .3516687
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  3,    85) =    0.19
                                                       Prob > F      =  0.9045
                                                       R-squared     =  0.0042
                                                       Root MSE      =  .38371

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       macua |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0031454   .1174167    -0.03   0.979    -.2366013    .2303105
     hotline |  -.0207696   .1053852    -0.20   0.844    -.2303037    .1887644
     verdade |  -.0626036   .0951264    -0.66   0.512    -.2517404    .1265331
       _cons |   .2014925   .0741109     2.72   0.008     .0541403    .3488448
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.19
            Prob > F =    0.9045
.90451465


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     255
                                                       F(  1,    39) =    0.10
                                                       Prob > F      =  0.7513
                                                       R-squared     =  0.0017
                                                       Root MSE      =  .22063

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       lomue |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0183792   .0575798    -0.32   0.751    -.1348454     .098087
       _cons |   .0597015   .0516199     1.16   0.254    -.0447096    .1641126
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.09
                                                       Prob > F      =  0.7646
                                                       R-squared     =  0.0013
                                                       Root MSE      =  .25583

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       lomue |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0186118   .0617888     0.30   0.765    -.1058373    .1430608
       _cons |   .0597015   .0515185     1.16   0.253    -.0440621    .1634651
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    0.35
                                                       Prob > F      =  0.5575
                                                       R-squared     =  0.0062
                                                       Root MSE      =  .20334

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       lomue |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0319237   .0540038    -0.59   0.558    -.1408327    .0769853
       _cons |   .0597015   .0515514     1.16   0.253    -.0442618    .1636648
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  3,    85) =    0.67
                                                       Prob > F      =  0.5746
                                                       R-squared     =  0.0078
                                                       Root MSE      =  .22415

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       lomue |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0183792   .0572287    -0.32   0.749    -.1321652    .0954068
     hotline |   .0186118   .0615329     0.30   0.763    -.1037322    .1409557
     verdade |  -.0319237   .0537458    -0.59   0.554    -.1387848    .0749374
       _cons |   .0597015   .0513051     1.16   0.248    -.0423069    .1617099
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.67
            Prob > F =    0.5746
.5745957


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     255
                                                       F(  1,    39) =    0.00
                                                       Prob > F      =  0.9761
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .39244

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      chuabo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0035155   .1167568     0.03   0.976    -.2326474    .2396784
       _cons |   .1865672   .0795191     2.35   0.024     .0257246    .3474097
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.31
                                                       Prob > F      =  0.5809
                                                       R-squared     =  0.0055
                                                       Root MSE      =  .36371

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      chuabo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   -.054037   .0971626    -0.56   0.581    -.2497325    .1416584
       _cons |   .1865672   .0793628     2.35   0.023     .0267222    .3464121
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    0.06
                                                       Prob > F      =  0.8061
                                                       R-squared     =  0.0013
                                                       Root MSE      =  .37908

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      chuabo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0268449   .1086947    -0.25   0.806    -.2460486    .1923587
       _cons |   .1865672   .0794135     2.35   0.023     .0264145    .3467198
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  3,    85) =    0.16
                                                       Prob > F      =  0.9229
                                                       R-squared     =  0.0041
                                                       Root MSE      =  .37138

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      chuabo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0035155   .1160448     0.03   0.976    -.2272127    .2342437
     hotline |   -.054037   .0967602    -0.56   0.578    -.2464223    .1383482
     verdade |  -.0268449   .1081754    -0.25   0.805    -.2419267    .1882368
       _cons |   .1865672   .0790342     2.36   0.021      .029426    .3437083
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.16
            Prob > F =    0.9229
.92289087


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     255
                                                       F(  0,    39) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.0255
                                                       Root MSE      =  .16193

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    chironga |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0522388    .018979    -2.75   0.009    -.0906274   -.0138502
       _cons |   .0522388    .018979     2.75   0.009     .0138502    .0906274
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.84
                                                       Prob > F      =  0.3632
                                                       R-squared     =  0.0031
                                                       Root MSE      =  .19631

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    chironga |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0221183   .0240764    -0.92   0.363    -.0706107    .0263741
       _cons |   .0522388   .0189417     2.76   0.008     .0140883    .0903893
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    1.68
                                                       Prob > F      =  0.2020
                                                       R-squared     =  0.0071
                                                       Root MSE      =  .18623

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    chironga |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0314055   .0242363    -1.30   0.202    -.0802826    .0174717
       _cons |   .0522388   .0189538     2.76   0.009     .0140149    .0904627
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  2,    85) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.0124
                                                       Root MSE      =  .16033

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    chironga |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0522388   .0188632    -2.77   0.007    -.0897439   -.0147337
     hotline |  -.0221183   .0239767    -0.92   0.359    -.0697905    .0255538
     verdade |  -.0314055   .0241205    -1.30   0.196    -.0793635    .0165526
       _cons |   .0522388   .0188632     2.77   0.007     .0147337    .0897439
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    4.58
            Prob > F =    0.0051
.00508374


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     255
                                                       F(  1,    39) =    0.98
                                                       Prob > F      =  0.3288
                                                       R-squared     =  0.0088
                                                       Root MSE      =  .08817

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     maconde |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0165289   .0167134     0.99   0.329    -.0172772    .0503351
       _cons |  -3.47e-18   8.50e-18    -0.41   0.685    -2.07e-17    1.37e-17
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    1.03
                                                       Prob > F      =  0.3159
                                                       R-squared     =  0.0221
                                                       Root MSE      =  .15985

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     maconde |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0481928   .0475146     1.01   0.316    -.0475066    .1438921
       _cons |  -1.39e-17   6.94e-18    -2.00   0.052    -2.79e-17    9.79e-20
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    2.77
                                                       Prob > F      =  0.1032
                                                       R-squared     =  0.0240
                                                       Root MSE      =  .15534

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     maconde |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0486111   .0291995     1.66   0.103    -.0102753    .1074975
       _cons |  -6.59e-17   4.16e-17    -1.58   0.121    -1.50e-16    1.80e-17
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  3,    85) =    1.61
                                                       Prob > F      =  0.1934
                                                       R-squared     =  0.0150
                                                       Root MSE      =  .17015

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     maconde |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0165289   .0166115     1.00   0.323    -.0164992    .0495571
     hotline |   .0481928   .0473179     1.02   0.311    -.0458878    .1422733
     verdade |   .0486111     .02906     1.67   0.098     -.009168    .1063902
       _cons |   6.94e-16          .        .       .            .           .
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    1.61
            Prob > F =    0.1934
.193409


note: results saved to balance_halves.xml

. 
. foreach i in $demo3 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & pt_dayselec>0 & pt_daysel
> ec<50, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & pt_dayselec>0 & pt_day
> selec<50, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & pt_dayselec>0 & pt_day
> selec<50, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0 & pt_dayselec>0 & pt_dayselec<50, 
> cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 14.         xml_tab $list1, below save(balance_halves.xml) append sheet("bal `i' 1")
 15.         estimates clear
 16. 
. }

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    0.89
                                                       Prob > F      =  0.3515
                                                       R-squared     =  0.0079
                                                       Root MSE      =   .4762

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cathol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.084781   .0899055    -0.94   0.351     -.266632      .09707
       _cons |   .3880597   .0603985     6.42   0.000     .2658922    .5102272
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.20
                                                       Prob > F      =  0.6533
                                                       R-squared     =  0.0016
                                                       Root MSE      =  .48312

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cathol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0386621   .0854965    -0.45   0.653     -.210861    .1335368
       _cons |   .3880597   .0602803     6.44   0.000      .266649    .5094704
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     279
                                                       F(  1,    43) =    0.31
                                                       Prob > F      =  0.5824
                                                       R-squared     =  0.0020
                                                       Root MSE      =  .48284

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cathol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0432321   .0780358    -0.55   0.582    -.2006062     .114142
       _cons |   .3880597   .0603184     6.43   0.000     .2664161    .5097033
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  3,    85) =    0.30
                                                       Prob > F      =  0.8237
                                                       R-squared     =  0.0036
                                                       Root MSE      =  .47699

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cathol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.084781   .0893571    -0.95   0.345    -.2624469    .0928848
     hotline |  -.0386621   .0851417    -0.45   0.651    -.2079466    .1306224
     verdade |  -.0432321   .0776628    -0.56   0.579    -.1976465    .1111823
       _cons |   .3880597   .0600301     6.46   0.000     .2687038    .5074156
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.30
            Prob > F =    0.8237
.82374124


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    0.01
                                                       Prob > F      =  0.9283
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .47808

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     protest |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0091754   .1013097     0.09   0.928    -.1957429    .2140937
       _cons |   .3432836   .0585849     5.86   0.000     .2247844    .4617827
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.04
                                                       Prob > F      =  0.8478
                                                       R-squared     =  0.0004
                                                       Root MSE      =  .47952

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     protest |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0181622   .0940749     0.19   0.848    -.1713144    .2076388
       _cons |   .3432836   .0584703     5.87   0.000     .2255185    .4610487
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     279
                                                       F(  1,    43) =    0.03
                                                       Prob > F      =  0.8584
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .47902

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     protest |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0153371   .0854651     0.18   0.858    -.1570197    .1876939
       _cons |   .3432836   .0585072     5.87   0.000     .2252925    .4612746
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  3,    85) =    0.02
                                                       Prob > F      =  0.9971
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .48001

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     protest |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0091754   .1006918     0.09   0.928    -.1910268    .2093777
     hotline |   .0181622   .0936845     0.19   0.847    -.1681076     .204432
     verdade |   .0153371   .0850566     0.18   0.857    -.1537782    .1844524
       _cons |   .3432836   .0582276     5.90   0.000     .2275116    .4590556
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.02
            Prob > F =    0.9971
.99710397


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    0.16
                                                       Prob > F      =  0.6875
                                                       R-squared     =  0.0029
                                                       Root MSE      =  .42218

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      muslim |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0451431   .1114041     0.41   0.688     -.180193    .2704793
       _cons |   .2089552   .0681943     3.06   0.004     .0710192    .3468913
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.10
                                                       Prob > F      =  0.7540
                                                       R-squared     =  0.0014
                                                       Root MSE      =  .41977

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      muslim |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0320086   .1015233     0.32   0.754    -.1724697     .236487
       _cons |   .2089552   .0680609     3.07   0.004     .0718736    .3460368
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     279
                                                       F(  1,    43) =    0.01
                                                       Prob > F      =  0.9063
                                                       R-squared     =  0.0002
                                                       Root MSE      =   .4123

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      muslim |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0117344   .0991023     0.12   0.906    -.1881244    .2115933
       _cons |   .2089552   .0681039     3.07   0.004     .0716106    .3462998
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  3,    85) =    0.07
                                                       Prob > F      =  0.9761
                                                       R-squared     =  0.0016
                                                       Root MSE      =  .42265

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      muslim |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0451431   .1107246     0.41   0.685     -.175007    .2652933
     hotline |   .0320086   .1011019     0.32   0.752     -.169009    .2330262
     verdade |   .0117344   .0986286     0.12   0.906    -.1843657    .2078346
       _cons |   .2089552   .0677784     3.08   0.003     .0741937    .3437168
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.07
            Prob > F =    0.9761
.9761216


note: results saved to balance_halves.xml

. 
. foreach i in $demo4 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & pt_dayselec>0 & pt_daysel
> ec<50, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & pt_dayselec>0 & pt_day
> selec<50, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & pt_dayselec>0 & pt_day
> selec<50, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0 & pt_dayselec>0 & pt_dayselec<50, 
> cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 14.         xml_tab $list1, below save(balance_halves.xml) append sheet("bal `i' 1")
 15.         estimates clear
 16. 
. }

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    2.18
                                                       Prob > F      =  0.1482
                                                       R-squared     =  0.0134
                                                       Root MSE      =  .41485

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         job |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0965256   .0654418    -1.47   0.148    -.2288941     .035843
       _cons |   .2686567    .049035     5.48   0.000      .169474    .3678394
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.15
                                                       Prob > F      =  0.7049
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .43616

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         job |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0276929   .0726479    -0.38   0.705    -.1740133    .1186276
       _cons |   .2686567   .0489391     5.49   0.000     .1700884    .3672251
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     279
                                                       F(  1,    43) =    0.85
                                                       Prob > F      =  0.3614
                                                       R-squared     =  0.0041
                                                       Root MSE      =  .42783

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         job |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0548636   .0594683    -0.92   0.361    -.1747929    .0650657
       _cons |   .2686567     .04897     5.49   0.000     .1698993    .3674142
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  3,    85) =    0.80
                                                       Prob > F      =  0.4947
                                                       R-squared     =  0.0066
                                                       Root MSE      =  .41817

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         job |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0965256   .0650426    -1.48   0.141    -.2258478    .0327966
     hotline |  -.0276929   .0723464    -0.38   0.703    -.1715369    .1161512
     verdade |  -.0548636   .0591841    -0.93   0.357    -.1725374    .0628102
       _cons |   .2686567   .0487359     5.51   0.000     .1717566    .3655568
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.80
            Prob > F =    0.4947
.49467707


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    0.16
                                                       Prob > F      =  0.6878
                                                       R-squared     =  0.0015
                                                       Root MSE      =  .45657

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       agric |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .035356   .0873407     0.40   0.688    -.1413072    .2120192
       _cons |   .2761194   .0701301     3.94   0.000      .134268    .4179708
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.04
                                                       Prob > F      =  0.8447
                                                       R-squared     =  0.0004
                                                       Root MSE      =  .45362

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       agric |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0190613   .0967615     0.20   0.845    -.1758264     .213949
       _cons |   .2761194   .0699928     3.94   0.000     .1351466    .4170922
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     279
                                                       F(  1,    43) =    0.03
                                                       Prob > F      =  0.8707
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .44489

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       agric |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0140504   .0858299    -0.16   0.871    -.1871429    .1590421
       _cons |   .2761194   .0700371     3.94   0.000     .1348762    .4173626
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  3,    85) =    0.17
                                                       Prob > F      =  0.9150
                                                       R-squared     =  0.0016
                                                       Root MSE      =  .45299

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       agric |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .035356   .0868079     0.41   0.685    -.1372414    .2079535
     hotline |   .0190613   .0963599     0.20   0.844     -.172528    .2106506
     verdade |  -.0140504   .0854196    -0.16   0.870    -.1838876    .1557867
       _cons |   .2761194   .0697023     3.96   0.000     .1375326    .4147062
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.17
            Prob > F =    0.9150
.91495561


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     255
                                                       F(  1,    39) =    2.64
                                                       Prob > F      =  0.1123
                                                       R-squared     =  0.0112
                                                       Root MSE      =  .20282

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         com |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0430174   .0264769     1.62   0.112    -.0105372    .0965719
       _cons |   .0225564   .0119909     1.88   0.067    -.0016975    .0468103
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     299
                                                       F(  1,    45) =    0.96
                                                       Prob > F      =  0.3331
                                                       R-squared     =  0.0029
                                                       Root MSE      =  .18013

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         com |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0196123   .0200433     0.98   0.333    -.0207571    .0599816
       _cons |   .0225564   .0119674     1.88   0.066    -.0015472      .04666
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    0.07
                                                       Prob > F      =  0.7977
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .15722

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         com |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0050298   .0195019     0.26   0.798    -.0342995    .0443591
       _cons |   .0225564    .011975     1.88   0.066    -.0015935    .0467063
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    85) =    1.05
                                                       Prob > F      =  0.3751
                                                       R-squared     =  0.0067
                                                       Root MSE      =  .19331

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         com |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0430174   .0263153     1.63   0.106    -.0093045    .0953393
     hotline |   .0196123   .0199601     0.98   0.329    -.0200738    .0592983
     verdade |   .0050298   .0194086     0.26   0.796    -.0335597    .0436193
       _cons |   .0225564   .0119177     1.89   0.062    -.0011392     .046252
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    1.05
            Prob > F =    0.3751
.37508497


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     255
                                                       F(  1,    39) =    7.01
                                                       Prob > F      =  0.0116
                                                       R-squared     =  0.0198
                                                       Root MSE      =  .18341

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         art |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0519537   .0196176    -2.65   0.012    -.0916339   -.0122734
       _cons |   .0601504    .017843     3.37   0.002     .0240595    .0962412
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     299
                                                       F(  1,    45) =    0.06
                                                       Prob > F      =  0.8086
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .23233

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         art |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0059335   .0243562    -0.24   0.809    -.0549894    .0431223
       _cons |   .0601504    .017808     3.38   0.002     .0242832    .0960176
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    0.31
                                                       Prob > F      =  0.5830
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .25313

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         art |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0157117   .0284017     0.55   0.583    -.0415657    .0729891
       _cons |   .0601504   .0178193     3.38   0.002     .0242143    .0960864
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    85) =    5.50
                                                       Prob > F      =  0.0017
                                                       R-squared     =  0.0118
                                                       Root MSE      =  .21995

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         art |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0519537   .0194978    -2.66   0.009    -.0907206   -.0131867
     hotline |  -.0059335   .0242551    -0.24   0.807    -.0541591     .042292
     verdade |   .0157117   .0282659     0.56   0.580    -.0404884    .0719118
       _cons |   .0601504   .0177341     3.39   0.001     .0248902    .0954105
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    5.50
            Prob > F =    0.0017
.001682


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     255
                                                       F(  1,    39) =    1.47
                                                       Prob > F      =  0.2329
                                                       R-squared     =  0.0063
                                                       Root MSE      =  .22013

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         man |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0348823   .0287872    -1.21   0.233    -.0931099    .0233454
       _cons |   .0676692   .0250403     2.70   0.010     .0170204     .118318
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     299
                                                       F(  1,    45) =    0.18
                                                       Prob > F      =  0.6693
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .23856

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         man |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0134523   .0312912    -0.43   0.669     -.076476    .0495714
       _cons |   .0676692   .0249912     2.71   0.010     .0173342    .1180041
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    0.76
                                                       Prob > F      =  0.3883
                                                       R-squared     =  0.0027
                                                       Root MSE      =   .2761

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         man |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0288826   .0331418     0.87   0.388    -.0379543    .0957194
       _cons |   .0676692   .0250071     2.71   0.010     .0172376    .1181007
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    85) =    2.14
                                                       Prob > F      =  0.1011
                                                       R-squared     =  0.0086
                                                       Root MSE      =  .24386

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         man |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0348823   .0286115    -1.22   0.226    -.0917697    .0220051
     hotline |  -.0134523   .0311613    -0.43   0.667    -.0754093    .0485046
     verdade |   .0288826   .0329834     0.88   0.384    -.0366972    .0944623
       _cons |   .0676692   .0248875     2.72   0.008     .0181862    .1171522
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    2.14
            Prob > F =    0.1011
.10112248


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     255
                                                       F(  1,    39) =    2.22
                                                       Prob > F      =  0.1447
                                                       R-squared     =  0.0094
                                                       Root MSE      =  .15146

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       assal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0293973   .0197494    -1.49   0.145    -.0693442    .0105497
       _cons |    .037594   .0180194     2.09   0.044     .0011464    .0740416
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     299
                                                       F(  1,    45) =    0.10
                                                       Prob > F      =  0.7512
                                                       R-squared     =  0.0004
                                                       Root MSE      =  .18036

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       assal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0074735   .0234265    -0.32   0.751    -.0546569    .0397099
       _cons |    .037594    .017984     2.09   0.042     .0013723    .0738157
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    2.54
                                                       Prob > F      =  0.1183
                                                       R-squared     =  0.0111
                                                       Root MSE      =  .14503

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       assal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0306974    .019262    -1.59   0.118    -.0695429    .0081481
       _cons |    .037594   .0179954     2.09   0.043     .0013027    .0738852
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    85) =    1.43
                                                       Prob > F      =  0.2396
                                                       R-squared     =  0.0084
                                                       Root MSE      =  .14395

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       assal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0293973   .0196289    -1.50   0.138    -.0684247    .0096302
     hotline |  -.0074735   .0233292    -0.32   0.749    -.0538583    .0389113
     verdade |  -.0306974   .0191699    -1.60   0.113    -.0688123    .0074174
       _cons |    .037594   .0179094     2.10   0.039     .0019853    .0732026
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    1.43
            Prob > F =    0.2396
.23955036


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     255
                                                       F(  1,    39) =    0.23
                                                       Prob > F      =  0.6364
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .21252

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         tea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.011648   .0244432    -0.48   0.636    -.0610891    .0377931
       _cons |   .0526316   .0192413     2.74   0.009     .0137123    .0915508
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     299
                                                       F(  1,    45) =    0.05
                                                       Prob > F      =  0.8233
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .23232

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         tea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0076094   .0338866     0.22   0.823    -.0606416    .0758604
       _cons |   .0526316   .0192036     2.74   0.009     .0139535    .0913097
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    0.60
                                                       Prob > F      =  0.4440
                                                       R-squared     =  0.0020
                                                       Root MSE      =  .20376

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         tea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0181488   .0234915    -0.77   0.444    -.0655239    .0292262
       _cons |   .0526316   .0192158     2.74   0.009     .0138793    .0913839
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    85) =    0.35
                                                       Prob > F      =  0.7884
                                                       R-squared     =  0.0023
                                                       Root MSE      =  .21364

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         tea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.011648   .0242941    -0.48   0.633    -.0599511    .0366551
     hotline |   .0076094   .0337459     0.23   0.822    -.0594865    .0747052
     verdade |  -.0181488   .0233791    -0.78   0.440    -.0646328    .0283351
       _cons |   .0526316   .0191239     2.75   0.007     .0146081     .090655
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.35
            Prob > F =    0.7884
.78841084


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     255
                                                       F(  1,    39) =    0.03
                                                       Prob > F      =  0.8729
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .21259

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      puboff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0040675    .025259     0.16   0.873    -.0470237    .0551588
       _cons |   .0451128   .0186423     2.42   0.020     .0074052    .0828204
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     299
                                                       F(  1,    45) =    0.16
                                                       Prob > F      =  0.6955
                                                       R-squared     =  0.0005
                                                       Root MSE      =  .19688

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      puboff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0089682   .0227665    -0.39   0.695    -.0548223    .0368859
       _cons |   .0451128   .0186058     2.42   0.019     .0076388    .0825867
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    0.38
                                                       Prob > F      =  0.5396
                                                       R-squared     =  0.0022
                                                       Root MSE      =  .18669

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      puboff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0175266   .0283402    -0.62   0.540    -.0746801     .039627
       _cons |   .0451128   .0186176     2.42   0.020     .0075669    .0826587
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    85) =    0.27
                                                       Prob > F      =  0.8500
                                                       R-squared     =  0.0018
                                                       Root MSE      =   .1938

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      puboff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0040675   .0251049     0.16   0.872    -.0458477    .0539828
     hotline |  -.0089682    .022672    -0.40   0.693    -.0540462    .0361098
     verdade |  -.0175266   .0282047    -0.62   0.536    -.0736051     .038552
       _cons |   .0451128   .0185285     2.43   0.017     .0082731    .0819525
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.27
            Prob > F =    0.8500
.84995861


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     255
                                                       F(  1,    39) =    0.18
                                                       Prob > F      =  0.6749
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .21252

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        stud |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.011648   .0275578    -0.42   0.675    -.0673888    .0440928
       _cons |   .0526316   .0195572     2.69   0.010     .0130734    .0921897
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     299
                                                       F(  1,    45) =    1.59
                                                       Prob > F      =  0.2143
                                                       R-squared     =  0.0057
                                                       Root MSE      =  .18834

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        stud |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0285352   .0226517    -1.26   0.214    -.0741581    .0170877
       _cons |   .0526316   .0195189     2.70   0.010     .0133186    .0919446
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     278
                                                       F(  1,    43) =    0.57
                                                       Prob > F      =  0.4529
                                                       R-squared     =  0.0020
                                                       Root MSE      =  .20376

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        stud |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0181488    .023962    -0.76   0.453    -.0664728    .0301751
       _cons |   .0526316   .0195312     2.69   0.010     .0132431    .0920201
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    85) =    0.60
                                                       Prob > F      =  0.6171
                                                       R-squared     =  0.0031
                                                       Root MSE      =  .18939

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        stud |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.011648   .0273896    -0.43   0.672    -.0661058    .0428098
     hotline |  -.0285352   .0225577    -1.26   0.209    -.0733859    .0163155
     verdade |  -.0181488   .0238474    -0.76   0.449    -.0655639    .0292662
       _cons |   .0526316   .0194378     2.71   0.008      .013984    .0912792
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.60
            Prob > F =    0.6171
.61714828


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    0.64
                                                       Prob > F      =  0.4286
                                                       R-squared     =  0.0028
                                                       Root MSE      =  .36027

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         dom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0378028   .0472627     0.80   0.429    -.0577951    .1334007
       _cons |   .1343284   .0307089     4.37   0.000     .0722137     .196443
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.11
                                                       Prob > F      =  0.7417
                                                       R-squared     =  0.0004
                                                       Root MSE      =  .33364

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         dom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0138464   .0417437    -0.33   0.742    -.0979225    .0702297
       _cons |   .1343284   .0306488     4.38   0.000     .0725985    .1960582
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     279
                                                       F(  1,    43) =    0.26
                                                       Prob > F      =  0.6104
                                                       R-squared     =  0.0012
                                                       Root MSE      =  .35513

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         dom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0242923   .0473363     0.51   0.610    -.0711705    .1197552
       _cons |   .1343284   .0306682     4.38   0.000     .0724801    .1961766
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  3,    85) =    0.52
                                                       Prob > F      =  0.6673
                                                       R-squared     =  0.0033
                                                       Root MSE      =  .35238

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         dom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0378028   .0469744     0.80   0.423     -.055595    .1312006
     hotline |  -.0138464   .0415704    -0.33   0.740    -.0964996    .0688067
     verdade |   .0242923   .0471101     0.52   0.607    -.0693751    .1179598
       _cons |   .1343284   .0305216     4.40   0.000     .0736433    .1950134
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.52
            Prob > F =    0.6673
.66726527


note: results saved to balance_halves.xml

. 
. foreach i in $demo5 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & pt_dayselec>0 & pt_daysel
> ec<50, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & pt_dayselec>0 & pt_day
> selec<50, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & pt_dayselec>0 & pt_day
> selec<50, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0 & pt_dayselec>0 & pt_dayselec<50, 
> cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 14.         xml_tab $list1, below save(balance_halves.xml) append sheet("bal `i' 1")
 15.         estimates clear
 16. 
. }

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    0.21
                                                       Prob > F      =  0.6487
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .37512

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       house |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0236115   .0514341    -0.46   0.649    -.1276467    .0804238
       _cons |   .8432836   .0361941    23.30   0.000     .7700741    .9164931
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.02
                                                       Prob > F      =  0.8944
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .36782

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       house |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0059342    .044465    -0.13   0.894    -.0954913     .083623
       _cons |   .8432836   .0361233    23.34   0.000     .7705275    .9160396
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     279
                                                       F(  1,    43) =    0.37
                                                       Prob > F      =  0.5487
                                                       R-squared     =  0.0015
                                                       Root MSE      =  .37849

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       house |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0294905   .0487909    -0.60   0.549    -.1278867    .0689058
       _cons |   .8432836   .0361461    23.33   0.000      .770388    .9161792
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  3,    85) =    0.18
                                                       Prob > F      =  0.9087
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .37772

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       house |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0236115   .0511204    -0.46   0.645    -.1252525    .0780296
     hotline |  -.0059342   .0442805    -0.13   0.894    -.0939756    .0821073
     verdade |  -.0294905   .0485577    -0.61   0.545    -.1260362    .0670552
       _cons |   .8432836   .0359734    23.44   0.000     .7717589    .9148083
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.18
            Prob > F =    0.9087
.90872199


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    1.37
                                                       Prob > F      =  0.2482
                                                       R-squared     =  0.0095
                                                       Root MSE      =  .49699

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        land |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0970149    .082756    -1.17   0.248    -.2644047    .0703749
       _cons |   .5970149   .0522717    11.42   0.000     .4912854    .7027444
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.00
                                                       Prob > F      =  0.9928
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .49221

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        land |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0006294   .0697305    -0.01   0.993    -.1410738    .1398151
       _cons |   .5970149   .0521694    11.44   0.000     .4919404    .7020895
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     279
                                                       F(  1,    43) =    0.17
                                                       Prob > F      =  0.6780
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .49498

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        land |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0314977   .0753489    -0.42   0.678    -.1834532    .1204579
       _cons |   .5970149   .0522024    11.44   0.000     .4917388     .702291
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  3,    85) =    0.60
                                                       Prob > F      =  0.6166
                                                       R-squared     =  0.0058
                                                       Root MSE      =  .49567

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        land |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0970149   .0822512    -1.18   0.241    -.2605524    .0665225
     hotline |  -.0006294   .0694411    -0.01   0.993    -.1386969    .1374381
     verdade |  -.0314977   .0749888    -0.42   0.676    -.1805954       .1176
       _cons |   .5970149   .0519529    11.49   0.000     .4937187    .7003111
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.60
            Prob > F =    0.6166
.61660208


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    0.14
                                                       Prob > F      =  0.7055
                                                       R-squared     =  0.0006
                                                       Root MSE      =  .42012

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cattle |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .021287   .0559073     0.38   0.705    -.0917962    .1343702
       _cons |   .2164179   .0393456     5.50   0.000     .1368338     .296002
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.41
                                                       Prob > F      =  0.5267
                                                       R-squared     =  0.0025
                                                       Root MSE      =  .42799

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cattle |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0426182   .0667944     0.64   0.527    -.0919126     .177149
       _cons |   .2164179   .0392686     5.51   0.000     .1373268     .295509
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     279
                                                       F(  1,    43) =    0.16
                                                       Prob > F      =  0.6926
                                                       R-squared     =  0.0009
                                                       Root MSE      =  .42177

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cattle |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0249614   .0627117     0.40   0.693    -.1015088    .1514316
       _cons |   .2164179   .0392935     5.51   0.000     .1371751    .2956607
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  3,    85) =    0.15
                                                       Prob > F      =  0.9300
                                                       R-squared     =  0.0013
                                                       Root MSE      =  .42823

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cattle |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .021287   .0555663     0.38   0.703    -.0891936    .1317677
     hotline |   .0426182   .0665171     0.64   0.523    -.0896357    .1748721
     verdade |   .0249614   .0624119     0.40   0.690    -.0991302     .149053
       _cons |   .2164179   .0391057     5.53   0.000     .1386654    .2941704
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.15
            Prob > F =    0.9300
.93004987


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     256
                                                       F(  1,    39) =    0.72
                                                       Prob > F      =  0.4011
                                                       R-squared     =  0.0067
                                                       Root MSE      =  .41364

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         cel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0675312   .0795568    -0.85   0.401    -.2284501    .0933877
       _cons |   .8134328   .0447415    18.18   0.000     .7229346    .9039311
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     300
                                                       F(  1,    45) =    0.47
                                                       Prob > F      =  0.4968
                                                       R-squared     =  0.0043
                                                       Root MSE      =  .41246

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         cel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0543967   .0794066    -0.69   0.497    -.2143297    .1055363
       _cons |   .8134328   .0446539    18.22   0.000     .7234952    .9033705
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     279
                                                       F(  1,    43) =    0.37
                                                       Prob > F      =  0.5448
                                                       R-squared     =  0.0026
                                                       Root MSE      =  .40674

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         cel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   -.041019   .0671979    -0.61   0.545    -.1765365    .0944984
       _cons |   .8134328   .0446822    18.20   0.000     .7233226     .903543
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     567
                                                       F(  3,    85) =    0.32
                                                       Prob > F      =  0.8103
                                                       R-squared     =  0.0034
                                                       Root MSE      =  .41999

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         cel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0675312   .0790716    -0.85   0.395    -.2247466    .0896842
     hotline |  -.0543967    .079077    -0.69   0.493    -.2116229    .1028295
     verdade |   -.041019   .0668767    -0.61   0.541    -.1739878    .0919497
       _cons |   .8134328   .0444686    18.29   0.000     .7250173    .9018483
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.32
            Prob > F =    0.8103
.8103087


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     239
                                                       F(  1,    39) =    0.12
                                                       Prob > F      =  0.7287
                                                       R-squared     =  0.0007
                                                       Root MSE      =   204.8

                                    (Std. Err. adjusted for 40 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 expenditure |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |     -10.93   31.28419    -0.35   0.729    -74.20824    52.34824
       _cons |   155.1323   18.29096     8.48   0.000     118.1353    192.1292
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     281
                                                       F(  1,    45) =    0.02
                                                       Prob > F      =  0.9017
                                                       R-squared     =  0.0001
                                                       Root MSE      =  185.27

                                    (Std. Err. adjusted for 46 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 expenditure |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -3.297867   26.55594    -0.12   0.902    -56.78427    50.18854
       _cons |   155.1323   18.25467     8.50   0.000     118.3655    191.8991
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     260
                                                       F(  1,    43) =    0.46
                                                       Prob > F      =  0.5012
                                                       R-squared     =  0.0027
                                                       Root MSE      =  168.72

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 expenditure |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   -17.4251   25.69023    -0.68   0.501    -69.23439     34.3842
       _cons |   155.1323   18.26655     8.49   0.000     118.2943    191.9703
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     528
                                                       F(  3,    85) =    0.18
                                                       Prob > F      =  0.9095
                                                       R-squared     =  0.0013
                                                       Root MSE      =  192.59

                                    (Std. Err. adjusted for 86 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 expenditure |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |     -10.93   31.09512    -0.35   0.726    -72.75544    50.89544
     hotline |  -3.297867   26.44792    -0.12   0.901    -55.88341    49.28768
     verdade |   -17.4251   25.56909    -0.68   0.497     -68.2633    33.41311
       _cons |   155.1323   18.18042     8.53   0.000     118.9847    191.2798
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    85) =    0.18
            Prob > F =    0.9095
.90950051


note: results saved to balance_halves.xml

. 
. foreach i in $demo6 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & pt_dayselec>0 & pt_daysel
> ec<50, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & pt_dayselec>0 & pt_day
> selec<50, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & pt_dayselec>0 & pt_day
> selec<50, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0 & pt_dayselec>0 & pt_dayselec<50, 
> cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 14.         xml_tab $list1, below save(balance_halves.xml) append sheet("bal `i' 1")
 15.         estimates clear
 16. 
. }

Linear regression                                      Number of obs =     225
                                                       F(  1,    36) =    0.06
                                                       Prob > F      =  0.8011
                                                       R-squared     =  0.0015
                                                       Root MSE      =    .861

                                    (Std. Err. adjusted for 37 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
netmean_dist |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0663888   .2616404     0.25   0.801    -.4642425      .59702
       _cons |   1.281002   .1807256     7.09   0.000     .9144732     1.64753
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     260
                                                       F(  1,    41) =    0.58
                                                       Prob > F      =  0.4496
                                                       R-squared     =  0.0116
                                                       Root MSE      =  .75443

                                    (Std. Err. adjusted for 42 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
netmean_dist |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.1656314    .216955    -0.76   0.450    -.6037809    .2725181
       _cons |   1.281002   .1803729     7.10   0.000     .9167313    1.645272
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     221
                                                       F(  1,    37) =    0.69
                                                       Prob > F      =  0.4129
                                                       R-squared     =  0.0133
                                                       Root MSE      =  .75935

                                    (Std. Err. adjusted for 38 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
netmean_dist |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.1756975   .2121678    -0.83   0.413    -.6055902    .2541953
       _cons |   1.281002   .1806669     7.09   0.000     .9149357    1.647068
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     496
                                                       F(  3,    78) =    0.61
                                                       Prob > F      =  0.6125
                                                       R-squared     =  0.0194
                                                       Root MSE      =  .74812

                                    (Std. Err. adjusted for 79 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
netmean_dist |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0663888    .259938     0.26   0.799    -.4511081    .5838856
     hotline |  -.1656314   .2159649    -0.77   0.445    -.5955845    .2643218
     verdade |  -.1756975   .2108558    -0.83   0.407    -.5954791    .2440842
       _cons |   1.281002   .1795497     7.13   0.000     .9235456    1.638458
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    78) =    0.61
            Prob > F =    0.6125
.61249287


note: results saved to balance_halves.xml

. 
. global list2=""

. matrix define fpvalue_1=(fsex_1 \ fage_1 \ fhead_1 \ fhousen_1 \ fsingle_1 \ fmarriedunion_1 \
>  fnoschl_1 \ finformalschl_1 \ flit_1 \ fprim5y_1 \ fsec10y_1 \ fchang_1 \ fmacua_1 \ flomue_1
>  \ fchuabo_1 \ fchironga_1 \ fmaconde_1 \ fcathol_1 \ fprotest_1 \ fmuslim_1 \ fjob_1 \ fagric
> _1 \ fcom_1 \ fart_1 \ fman_1 \ fassal_1 \ ftea_1 \ fpuboff_1 \ fstud_1 \ fdom_1 \ fhouse_1 \ 
> fland_1 \ fcattle_1 \ fcel_1 \ fexpenditure_1 \ fnetmean_dist_1)

. matrix rownames fpvalue_1 = "sex" "age" "head" "housen" "single" "marriedunion" "noschl" "info
> rmalschl" "lit" "prim5y" "sec10y" "chang" "macua" "lomue" "chuabo" "chironga" "maconde" "catho
> l" "protest" "muslim" "job" "agric" "com" "art" "man" "assal" "tea" "puboff" "stud" "dom" "hou
> se" "land" "cattle" "cel" "expenditure" "netmean_dist"

. matrix fpvalue= (fpvalue_1)

. global list2="$list2" + " fpvalue"

. xml_tab $list2, save(balance_halves.xml) append sheet("fpvalue demo 1") 


note: results saved to balance_halves.xml

. estimates clear

. 
. *half2
. 
. foreach i in $demo1 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & pt_dayselec>=50 & pt_days
> elec<=100, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & pt_dayselec>=50 & pt_d
> ayselec<=100, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & pt_dayselec>=50 & pt_d
> ayselec<=100, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0 & pt_dayselec>=50 & pt_dayselec<=1
> 00, cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 14.         xml_tab $list1, below save(balance_halves.xml) append sheet("bal `i' 2")
 15.         estimates clear
 16. 
. }

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    4.04
                                                       Prob > F      =  0.0496
                                                       R-squared     =  0.0101
                                                       Root MSE      =  .49187

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         sex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0995859   .0495201    -2.01   0.050    -.1990017   -.0001701
       _cons |   .4710145   .0366763    12.84   0.000     .3973837    .5446452
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.51
                                                       Prob > F      =  0.4811
                                                       R-squared     =  0.0017
                                                       Root MSE      =   .5014

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         sex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0409855   .0576633     0.71   0.481    -.0753037    .1572747
       _cons |   .4710145   .0367531    12.82   0.000     .3968947    .5451343
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.25
                                                       Prob > F      =  0.6178
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .50145

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         sex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0289855   .0576672     0.50   0.618    -.0873115    .1452825
       _cons |   .4710145   .0367523    12.82   0.000     .3968963    .5451326
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    3.06
                                                       Prob > F      =  0.0319
                                                       R-squared     =  0.0137
                                                       Root MSE      =  .49638

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         sex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0995859   .0493568    -2.02   0.047    -.1975987   -.0015732
     hotline |   .0409855   .0573529     0.71   0.477    -.0729061    .1548771
     verdade |   .0289855    .057358     0.51   0.615    -.0849162    .1428872
       _cons |   .4710145   .0365553    12.88   0.000     .3984229    .5436061
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    3.06
            Prob > F =    0.0319
.031914


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     310
                                                       F(  1,    51) =    0.01
                                                       Prob > F      =  0.9302
                                                       R-squared     =  0.0000
                                                       Root MSE      =  12.889

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1623141      1.844     0.09   0.930    -3.539671    3.864299
       _cons |   35.78309    1.54859    23.11   0.000     32.67416    38.89201
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     260
                                                       F(  1,    43) =    5.01
                                                       Prob > F      =  0.0305
                                                       R-squared     =  0.0330
                                                       Root MSE      =  14.644

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   5.394331   2.410398     2.24   0.030     .5333009    10.25536
       _cons |   35.78309   1.551844    23.06   0.000      32.6535    38.91268
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.03
                                                       Prob > F      =  0.8558
                                                       R-squared     =  0.0002
                                                       Root MSE      =  13.253

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.3618284   1.979506    -0.18   0.856    -4.353882    3.630225
       _cons |   35.78309   1.551809    23.06   0.000     32.65357    38.91261
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     561
                                                       F(  3,    93) =    2.60
                                                       Prob > F      =  0.0570
                                                       R-squared     =  0.0265
                                                       Root MSE      =   13.76

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1623141   1.837932     0.09   0.930    -3.487454    3.812083
     hotline |   5.394331   2.397427     2.25   0.027     .6335158    10.15515
     verdade |  -.3618284   1.968898    -0.18   0.855    -4.271669    3.548012
       _cons |   35.78309   1.543493    23.18   0.000     32.71802    38.84816
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    2.60
            Prob > F =    0.0570
.0570412


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    1.07
                                                       Prob > F      =  0.3069
                                                       R-squared     =  0.0025
                                                       Root MSE      =  .43339

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        head |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0439337   .0425687    -1.03   0.307     -.129394    .0415266
       _cons |   .7753623   .0334469    23.18   0.000     .7082149    .8425098
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.22
                                                       Prob > F      =  0.6444
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .42592

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        head |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0233623    .050264    -0.46   0.644    -.1247293    .0780047
       _cons |   .7753623    .033517    23.13   0.000     .7077689    .8429557
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.43
                                                       Prob > F      =  0.5132
                                                       R-squared     =  0.0013
                                                       Root MSE      =  .40896

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        head |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0293252    .044472     0.66   0.513    -.0603611    .1190115
       _cons |   .7753623   .0335162    23.13   0.000     .7077704    .8429543
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    1.24
                                                       Prob > F      =  0.3001
                                                       R-squared     =  0.0042
                                                       Root MSE      =  .42569

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        head |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0439337   .0424283    -1.04   0.303    -.1281879    .0403204
     hotline |  -.0233623   .0499934    -0.47   0.641    -.1226394    .0759147
     verdade |   .0293252   .0442336     0.66   0.509    -.0585139    .1171643
       _cons |   .7753623   .0333366    23.26   0.000     .7091625    .8415621
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    1.24
            Prob > F =    0.3001
.30007842


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    1.97
                                                       Prob > F      =  0.1669
                                                       R-squared     =  0.0096
                                                       Root MSE      =   2.365

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      housen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .4668116   .3328763     1.40   0.167    -.2014654    1.135089
       _cons |   5.373188   .2427006    22.14   0.000     4.885947     5.86043
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    5.66
                                                       Prob > F      =  0.0219
                                                       R-squared     =  0.0294
                                                       Root MSE      =  2.6215

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      housen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .9108116    .382903     2.38   0.022     .1386141    1.683009
       _cons |   5.373188    .243209    22.09   0.000     4.882711    5.863666
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.13
                                                       Prob > F      =  0.7163
                                                       R-squared     =  0.0009
                                                       Root MSE      =  2.3953

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      housen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .1463428   .4000424     0.37   0.716    -.6604196    .9531053
       _cons |   5.373188   .2432038    22.09   0.000     4.882721    5.863656
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    2.13
                                                       Prob > F      =  0.1013
                                                       R-squared     =  0.0170
                                                       Root MSE      =   2.553

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      housen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .4668116   .3317781     1.41   0.163    -.1920339    1.125657
     hotline |   .9108116    .380842     2.39   0.019     .1545349    1.667088
     verdade |   .1463428   .3978978     0.37   0.714    -.6438032    .9364889
       _cons |   5.373188   .2418999    22.21   0.000     4.892823    5.853554
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    2.13
            Prob > F =    0.1013
.10130093


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.07
                                                       Prob > F      =  0.7875
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .37333

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      single |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.013913   .0513432    -0.27   0.787    -.1169888    .0891627
       _cons |    .173913   .0430701     4.04   0.000     .0874462    .2603799
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.01
                                                       Prob > F      =  0.9164
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .37803

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      single |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   -.005913   .0560227    -0.11   0.916    -.1188935    .1070674
       _cons |    .173913   .0431603     4.03   0.000     .0868719    .2609542
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.22
                                                       Prob > F      =  0.6414
                                                       R-squared     =  0.0012
                                                       Root MSE      =   .3693

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      single |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0254755    .054316    -0.47   0.641    -.1350141    .0840631
       _cons |    .173913   .0431594     4.03   0.000     .0868738    .2609523
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    0.09
                                                       Prob > F      =  0.9641
                                                       R-squared     =  0.0006
                                                       Root MSE      =  .37014

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      single |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.013913   .0511738    -0.27   0.786    -.1155341     .087708
     hotline |   -.005913   .0557211    -0.11   0.916    -.1165641    .1047381
     verdade |  -.0254755   .0540248    -0.47   0.638     -.132758     .081807
       _cons |    .173913    .042928     4.05   0.000     .0886665    .2591596
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.09
            Prob > F =    0.9641
.96405489


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.12
                                                       Prob > F      =  0.7348
                                                       R-squared     =  0.0005
                                                       Root MSE      =  .44609

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
marriedunion |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0197516   .0579927     0.34   0.735    -.0966736    .1361767
       _cons |   .7173913   .0423234    16.95   0.000     .6324235    .8023591
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.08
                                                       Prob > F      =  0.7807
                                                       R-squared     =  0.0004
                                                       Root MSE      =   .4475

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
marriedunion |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0186087   .0664213     0.28   0.781    -.1153427    .1525601
       _cons |   .7173913   .0424121    16.91   0.000     .6318592    .8029234
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    1.96
                                                       Prob > F      =  0.1686
                                                       R-squared     =  0.0085
                                                       Root MSE      =  .42949

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
marriedunion |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0794837   .0567607     1.40   0.169    -.0349852    .1939525
       _cons |   .7173913   .0424112    16.92   0.000     .6318611    .8029215
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    0.78
                                                       Prob > F      =  0.5082
                                                       R-squared     =  0.0044
                                                       Root MSE      =  .43612

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
marriedunion |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0197516   .0578014     0.34   0.733    -.0950305    .1345336
     hotline |   .0186087   .0660638     0.28   0.779     -.112581    .1497984
     verdade |   .0794837   .0564564     1.41   0.163    -.0326275    .1915949
       _cons |   .7173913   .0421838    17.01   0.000     .6336227    .8011599
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.78
            Prob > F =    0.5082
.50820424


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.20
                                                       Prob > F      =  0.6594
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .41349

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      noschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0261698   .0590315    -0.44   0.659    -.1446804    .0923409
       _cons |   .2318841   .0413251     5.61   0.000     .1489204    .3148477
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     262
                                                       F(  1,    43) =    0.13
                                                       Prob > F      =  0.7199
                                                       R-squared     =  0.0007
                                                       Root MSE      =  .41662

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      noschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0222066   .0615311    -0.36   0.720    -.1462959    .1018827
       _cons |   .2318841    .041412     5.60   0.000     .1483688    .3153993
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    1.46
                                                       Prob > F      =  0.2337
                                                       R-squared     =  0.0056
                                                       Root MSE      =  .40264

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      noschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0600091    .049683    -1.21   0.234    -.1602044    .0401863
       _cons |   .2318841   .0414108     5.60   0.000     .1483713    .3153969
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  3,    93) =    0.56
                                                       Prob > F      =  0.6418
                                                       R-squared     =  0.0026
                                                       Root MSE      =  .40483

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      noschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0261698    .058837    -0.44   0.658    -.1430084    .0906688
     hotline |  -.0222066   .0611997    -0.36   0.718    -.1437372    .0993239
     verdade |  -.0600091   .0494169    -1.21   0.228    -.1581412    .0381231
       _cons |   .2318841    .041189     5.63   0.000     .1500909    .3136772
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.56
            Prob > F =    0.6418
.64183438


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    1.96
                                                       Prob > F      =  0.1674
                                                       R-squared     =  0.0091
                                                       Root MSE      =  .26058

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
informalschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0500207   .0357189    -1.40   0.167    -.1217293    .0216879
       _cons |   .1014493   .0315392     3.22   0.002     .0381317    .1647669
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     262
                                                       F(  1,    43) =    0.01
                                                       Prob > F      =  0.9364
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .30519

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
informalschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0033894   .0422044     0.08   0.936    -.0817239    .0885028
       _cons |   .1014493   .0316055     3.21   0.003     .0377107    .1651879
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.12
                                                       Prob > F      =  0.7260
                                                       R-squared     =  0.0006
                                                       Root MSE      =  .31275

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
informalschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0157382   .0446126     0.35   0.726    -.0742317    .1057081
       _cons |   .1014493   .0316046     3.21   0.003     .0377125     .165186
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  3,    93) =    1.86
                                                       Prob > F      =  0.1418
                                                       R-squared     =  0.0086
                                                       Root MSE      =  .28634

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
informalschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0500207   .0356012    -1.41   0.163    -.1207176    .0206762
     hotline |   .0033894   .0419772     0.08   0.936    -.0799689    .0867478
     verdade |   .0157382   .0443736     0.35   0.724     -.072379    .1038555
       _cons |   .1014493   .0314353     3.23   0.002      .039025    .1638736
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    1.86
            Prob > F =    0.1418
.14181505


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.20
                                                       Prob > F      =  0.6594
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .41349

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         lit |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0261698   .0590315     0.44   0.659    -.0923409    .1446804
       _cons |   .7681159   .0413251    18.59   0.000     .6851523    .8510796
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     262
                                                       F(  1,    43) =    0.13
                                                       Prob > F      =  0.7199
                                                       R-squared     =  0.0007
                                                       Root MSE      =  .41662

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         lit |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0222066   .0615311     0.36   0.720    -.1018827    .1462959
       _cons |   .7681159    .041412    18.55   0.000     .6846007    .8516312
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    1.46
                                                       Prob > F      =  0.2337
                                                       R-squared     =  0.0056
                                                       Root MSE      =  .40264

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         lit |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0600091    .049683     1.21   0.234    -.0401863    .1602044
       _cons |   .7681159   .0414108    18.55   0.000     .6846031    .8516287
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  3,    93) =    0.56
                                                       Prob > F      =  0.6418
                                                       R-squared     =  0.0026
                                                       Root MSE      =  .40483

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         lit |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0261698    .058837     0.44   0.658    -.0906688    .1430084
     hotline |   .0222066   .0611997     0.36   0.718    -.0993239    .1437372
     verdade |   .0600091   .0494169     1.21   0.228    -.0381231    .1581412
       _cons |   .7681159    .041189    18.65   0.000     .6863228    .8499091
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.56
            Prob > F =    0.6418
.64183438


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.29
                                                       Prob > F      =  0.5934
                                                       R-squared     =  0.0013
                                                       Root MSE      =  .45672

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      prim5y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0332091   .0618078     0.54   0.593    -.0908752    .1572934
       _cons |   .2753623   .0484866     5.68   0.000     .1780214    .3727033
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     262
                                                       F(  1,    43) =    0.07
                                                       Prob > F      =  0.7999
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .45185

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      prim5y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0149603   .0586481     0.26   0.800    -.1033148    .1332354
       _cons |   .2753623   .0485885     5.67   0.000     .1773742    .3733505
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.23
                                                       Prob > F      =  0.6350
                                                       R-squared     =  0.0010
                                                       Root MSE      =    .455

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      prim5y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0293252   .0613328     0.48   0.635    -.0943643    .1530146
       _cons |   .2753623   .0485871     5.67   0.000      .177377    .3733476
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  3,    93) =    0.13
                                                       Prob > F      =  0.9451
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .45773

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      prim5y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0332091   .0616042     0.54   0.591    -.0891246    .1555428
     hotline |   .0149603   .0583322     0.26   0.798     -.100876    .1307965
     verdade |   .0293252   .0610043     0.48   0.632    -.0918173    .1504677
       _cons |   .2753623   .0483269     5.70   0.000     .1793947    .3713299
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.13
            Prob > F =    0.9451
.94508152


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.04
                                                       Prob > F      =  0.8412
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .37901

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      sec10y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0104762    .052009     0.20   0.841    -.0939362    .1148886
       _cons |   .1666667   .0413861     4.03   0.000     .0835806    .2497527
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     262
                                                       F(  1,    43) =    0.14
                                                       Prob > F      =  0.7142
                                                       R-squared     =  0.0009
                                                       Root MSE      =  .36455

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      sec10y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0215054   .0583385    -0.37   0.714    -.1391562    .0961454
       _cons |   .1666667   .0414731     4.02   0.000     .0830282    .2503051
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.49
                                                       Prob > F      =  0.4884
                                                       R-squared     =  0.0022
                                                       Root MSE      =  .35839

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      sec10y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0338542   .0484404    -0.70   0.488    -.1315435    .0638351
       _cons |   .1666667   .0414719     4.02   0.000     .0830307    .2503027
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  3,    93) =    0.46
                                                       Prob > F      =  0.7087
                                                       R-squared     =  0.0023
                                                       Root MSE      =  .36516

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      sec10y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0104762   .0518376     0.20   0.840    -.0924631    .1134155
     hotline |  -.0215054   .0580243    -0.37   0.712    -.1367302    .0937194
     verdade |  -.0338542   .0481809    -0.70   0.484    -.1295319    .0618235
       _cons |   .1666667   .0412497     4.04   0.000     .0847529    .2485805
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.46
            Prob > F =    0.7087
.70865178


note: results saved to balance_halves.xml

. 
. foreach i in $demo2 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & pt_dayselec>=50 & pt_days
> elec<=100, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & pt_dayselec>=50 & pt_d
> ayselec<=100, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & pt_dayselec>=50 & pt_d
> ayselec<=100, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0 & pt_dayselec>=50 & pt_dayselec<=1
> 00, cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 14.         xml_tab $list1, below save(balance_halves.xml) append sheet("bal `i' 2")
 15.         estimates clear
 16. 
. }

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.04
                                                       Prob > F      =  0.8487
                                                       R-squared     =  0.0006
                                                       Root MSE      =   .4651

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       chang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0232298   .1211625    -0.19   0.849    -.2664737     .220014
       _cons |    .326087   .0961088     3.39   0.001     .1331405    .5190334
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.09
                                                       Prob > F      =  0.7672
                                                       R-squared     =  0.0019
                                                       Root MSE      =  .47705

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       chang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |    .041913   .1407173     0.30   0.767    -.2418704    .3256965
       _cons |    .326087   .0963101     3.39   0.002     .1318591    .5203148
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.05
                                                       Prob > F      =  0.8275
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .46484

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       chang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   -.029212   .1332037    -0.22   0.827    -.2978428    .2394189
       _cons |    .326087    .096308     3.39   0.002     .1318633    .5203106
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    0.11
                                                       Prob > F      =  0.9516
                                                       R-squared     =  0.0033
                                                       Root MSE      =  .46795

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       chang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0232298   .1207628    -0.19   0.848    -.2630407    .2165811
     hotline |    .041913   .1399598     0.30   0.765    -.2360195    .3198455
     verdade |   -.029212   .1324896    -0.22   0.826      -.29231    .2338861
       _cons |    .326087   .0957917     3.40   0.001     .1358636    .5163103
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.11
            Prob > F =    0.9516
.95158365


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.44
                                                       Prob > F      =  0.5115
                                                       R-squared     =  0.0082
                                                       Root MSE      =   .4303

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       macua |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0784265   .1186304    -0.66   0.512    -.3165869    .1597339
       _cons |   .2898551   .0911232     3.18   0.002     .1069175    .4727926
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.03
                                                       Prob > F      =  0.8711
                                                       R-squared     =  0.0006
                                                       Root MSE      =  .46004

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       macua |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0221449   .1357173     0.16   0.871    -.2515551     .295845
       _cons |   .2898551   .0913141     3.17   0.003     .1057026    .4740076
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.00
                                                       Prob > F      =  0.9468
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .45344

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       macua |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0086051   .1281914    -0.07   0.947    -.2671276    .2499174
       _cons |   .2898551   .0913121     3.17   0.003     .1057066    .4740036
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    0.28
                                                       Prob > F      =  0.8422
                                                       R-squared     =  0.0080
                                                       Root MSE      =    .443

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       macua |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0784265    .118239    -0.66   0.509    -.3132257    .1563727
     hotline |   .0221449   .1349868     0.16   0.870    -.2459121    .2902019
     verdade |  -.0086051   .1275041    -0.07   0.946     -.261803    .2445928
       _cons |   .2898551   .0908226     3.19   0.002     .1094994    .4702108
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.28
            Prob > F =    0.8422
.84216035


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.02
                                                       Prob > F      =  0.8985
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .37333

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       lomue |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.013913   .1085302    -0.13   0.898    -.2317966    .2039705
       _cons |    .173913   .0852814     2.04   0.047     .0027035    .3451226
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    1.88
                                                       Prob > F      =  0.1773
                                                       R-squared     =  0.0391
                                                       Root MSE      =  .31281

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       lomue |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   -.125913   .0918017    -1.37   0.177    -.3110488    .0592227
       _cons |    .173913     .08546     2.04   0.048     .0015665    .3462596
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.13
                                                       Prob > F      =  0.7237
                                                       R-squared     =  0.0032
                                                       Root MSE      =  .39744

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       lomue |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |    .044837   .1259854     0.36   0.724    -.2092368    .2989107
       _cons |    .173913   .0854582     2.04   0.048     .0015702    .3462559
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    1.87
                                                       Prob > F      =  0.1409
                                                       R-squared     =  0.0274
                                                       Root MSE      =  .35527

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       lomue |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.013913   .1081722    -0.13   0.898    -.2287215    .2008955
     hotline |   -.125913   .0913076    -1.38   0.171    -.3072318    .0554057
     verdade |    .044837     .12531     0.36   0.721    -.2040038    .2936777
       _cons |    .173913       .085     2.05   0.044     .0051198    .3427063
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    1.87
            Prob > F =    0.1409
.14092274


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    2.43
                                                       Prob > F      =  0.1249
                                                       R-squared     =  0.0078
                                                       Root MSE      =  .17574

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      chuabo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0312215   .0200136     1.56   0.125    -.0089575    .0714005
       _cons |   .0144928   .0096451     1.50   0.139    -.0048706    .0338561
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.01
                                                       Prob > F      =  0.9174
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .12285

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      chuabo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0015072   .0144397     0.10   0.917    -.0276132    .0306277
       _cons |   .0144928   .0096653     1.50   0.141    -.0049992    .0339847
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.00
                                                       Prob > F      =  0.9506
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .12216

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      chuabo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0011322   .0181661     0.06   0.951    -.0355031    .0377676
       _cons |   .0144928   .0096651     1.50   0.141    -.0049988    .0339843
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    0.89
                                                       Prob > F      =  0.4471
                                                       R-squared     =  0.0082
                                                       Root MSE      =  .15523

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      chuabo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0312215   .0199476     1.57   0.121    -.0083904    .0708335
     hotline |   .0015072    .014362     0.10   0.917    -.0270128    .0300273
     verdade |   .0011322   .0180687     0.06   0.950    -.0347486    .0370131
       _cons |   .0144928   .0096133     1.51   0.135    -.0045973    .0335828
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.89
            Prob > F =    0.4471
.44713576


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.01
                                                       Prob > F      =  0.9302
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .25644

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    chironga |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0038923   .0442071    -0.09   0.930    -.0926418    .0848571
       _cons |   .0724638   .0345298     2.10   0.041     .0031423    .1417853
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.31
                                                       Prob > F      =  0.5826
                                                       R-squared     =  0.0026
                                                       Root MSE      =  .23963

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    chironga |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0244638   .0441724    -0.55   0.583    -.1135459    .0646184
       _cons |   .0724638   .0346021     2.09   0.042     .0026819    .1422456
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.00
                                                       Prob > F      =  0.9672
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .25851

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    chironga |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0021513   .0520641    -0.04   0.967    -.1071485    .1028459
       _cons |   .0724638   .0346014     2.09   0.042     .0026834    .1422441
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    0.15
                                                       Prob > F      =  0.9286
                                                       R-squared     =  0.0014
                                                       Root MSE      =  .24788

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    chironga |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0038923   .0440612    -0.09   0.930    -.0913892    .0836045
     hotline |  -.0244638   .0439346    -0.56   0.579    -.1117092    .0627817
     verdade |  -.0021513    .051785    -0.04   0.967    -.1049859    .1006834
       _cons |   .0724638   .0344159     2.11   0.038     .0041206    .1408069
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.15
            Prob > F =    0.9286
.92858681


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.44
                                                       Prob > F      =  0.5083
                                                       R-squared     =  0.0050
                                                       Root MSE      =  .22679

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     maconde |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0323395   .0485436     0.67   0.508    -.0651159    .1297949
       _cons |   .0362319   .0215289     1.68   0.098    -.0069892     .079453
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.23
                                                       Prob > F      =  0.6328
                                                       R-squared     =  0.0013
                                                       Root MSE      =  .17228

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     maconde |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0122319   .0254221    -0.48   0.633    -.0635005    .0390367
       _cons |   .0362319    .021574     1.68   0.100    -.0072762      .07974
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.26
                                                       Prob > F      =  0.6105
                                                       R-squared     =  0.0014
                                                       Root MSE      =  .17132

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     maconde |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0127944   .0249319    -0.51   0.610    -.0630744    .0374857
       _cons |   .0362319   .0215735     1.68   0.100    -.0072753    .0797391
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    0.41
                                                       Prob > F      =  0.7447
                                                       R-squared     =  0.0096
                                                       Root MSE      =  .19719

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     maconde |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0323395   .0483835     0.67   0.506    -.0637404    .1284195
     hotline |  -.0122319   .0252853    -0.48   0.630    -.0624435    .0379797
     verdade |  -.0127944   .0247983    -0.52   0.607    -.0620389    .0364501
       _cons |   .0362319   .0214579     1.69   0.095    -.0063792     .078843
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.41
            Prob > F =    0.7447
.74472077


note: results saved to balance_halves.xml

. 
. foreach i in $demo3 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & pt_dayselec>=50 & pt_days
> elec<=100, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & pt_dayselec>=50 & pt_d
> ayselec<=100, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & pt_dayselec>=50 & pt_d
> ayselec<=100, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0 & pt_dayselec>=50 & pt_dayselec<=1
> 00, cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 14.         xml_tab $list1, below save(balance_halves.xml) append sheet("bal `i' 2")
 15.         estimates clear
 16. 
. }

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.18
                                                       Prob > F      =  0.6717
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .49745

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cathol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0311387   .0730571     0.43   0.672    -.1155294    .1778069
       _cons |   .4202899   .0561595     7.48   0.000      .307545    .5330347
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    3.29
                                                       Prob > F      =  0.0765
                                                       R-squared     =  0.0241
                                                       Root MSE      =  .47293

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cathol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.1482899   .0817019    -1.82   0.077    -.3130574    .0164777
       _cons |   .4202899   .0562771     7.47   0.000     .3067962    .5337835
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    1.75
                                                       Prob > F      =  0.1925
                                                       R-squared     =  0.0125
                                                       Root MSE      =  .48117

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cathol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.1077899   .0814078    -1.32   0.192    -.2719644    .0563847
       _cons |   .4202899   .0562759     7.47   0.000     .3067987     .533781
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    2.51
                                                       Prob > F      =  0.0632
                                                       R-squared     =  0.0236
                                                       Root MSE      =  .47949

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cathol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0311387    .072816     0.43   0.670    -.1134595    .1757369
     hotline |  -.1482899   .0812621    -1.82   0.071    -.3096603    .0130806
     verdade |  -.1077899   .0809714    -1.33   0.186     -.268583    .0530033
       _cons |   .4202899   .0559742     7.51   0.000     .3091362    .5314435
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    2.51
            Prob > F =    0.0632
.06315831


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.43
                                                       Prob > F      =  0.5153
                                                       R-squared     =  0.0032
                                                       Root MSE      =  .46703

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     protest |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0530021   .0808885     0.66   0.515    -.1093884    .2153926
       _cons |   .2898551   .0601957     4.82   0.000     .1690072    .4107029
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.79
                                                       Prob > F      =  0.3780
                                                       R-squared     =  0.0084
                                                       Root MSE      =  .47032

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     protest |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0861449   .0967165     0.89   0.378    -.1089024    .2811923
       _cons |   .2898551   .0603218     4.81   0.000     .1682047    .4115055
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.44
                                                       Prob > F      =  0.5109
                                                       R-squared     =  0.0044
                                                       Root MSE      =  .46704

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     protest |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0617074    .093085     0.66   0.511    -.1260163    .2494312
       _cons |   .2898551   .0603204     4.81   0.000     .1682073    .4115029
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    0.31
                                                       Prob > F      =  0.8158
                                                       R-squared     =  0.0042
                                                       Root MSE      =  .47414

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     protest |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0530021   .0806217     0.66   0.513    -.1070966    .2131007
     hotline |   .0861449   .0961959     0.90   0.373     -.104881    .2771708
     verdade |   .0617074   .0925859     0.67   0.507    -.1221499    .2455648
       _cons |   .2898551   .0599971     4.83   0.000     .1707128    .4089974
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.31
            Prob > F =    0.8158
.81584479


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.67
                                                       Prob > F      =  0.4185
                                                       R-squared     =  0.0054
                                                       Root MSE      =  .38874

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      muslim |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0573913   .0703644    -0.82   0.419    -.1986538    .0838712
       _cons |   .2173913   .0540082     4.03   0.000     .1089654    .3258172
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.14
                                                       Prob > F      =  0.7122
                                                       R-squared     =  0.0021
                                                       Root MSE      =  .42564

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      muslim |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0386087   .1039759     0.37   0.712    -.1710787    .2482961
       _cons |   .2173913   .0541213     4.02   0.000     .1082453    .3265373
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.39
                                                       Prob > F      =  0.5341
                                                       R-squared     =  0.0055
                                                       Root MSE      =  .43237

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      muslim |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0638587   .1018705     0.63   0.534    -.1415828    .2693002
       _cons |   .2173913   .0541201     4.02   0.000     .1082477     .326535
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    0.73
                                                       Prob > F      =  0.5393
                                                       R-squared     =  0.0130
                                                       Root MSE      =  .41477

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      muslim |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0573913   .0701323    -0.82   0.415    -.1966601    .0818775
     hotline |   .0386087   .1034162     0.37   0.710    -.1667555    .2439728
     verdade |   .0638587   .1013244     0.63   0.530    -.1373514    .2650688
       _cons |   .2173913     .05383     4.04   0.000     .1104956     .324287
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.73
            Prob > F =    0.5393
.53928924


note: results saved to balance_halves.xml

. 
. foreach i in $demo4 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & pt_dayselec>=50 & pt_days
> elec<=100, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & pt_dayselec>=50 & pt_d
> ayselec<=100, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & pt_dayselec>=50 & pt_d
> ayselec<=100, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0 & pt_dayselec>=50 & pt_dayselec<=1
> 00, cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 14.         xml_tab $list1, below save(balance_halves.xml) append sheet("bal `i' 2")
 15.         estimates clear
 16. 
. }

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.05
                                                       Prob > F      =  0.8263
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .41799

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         job |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0111801   .0506852     0.22   0.826    -.0905746    .1129349
       _cons |   .2173913   .0343427     6.33   0.000     .1484454    .2863373
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.34
                                                       Prob > F      =  0.5623
                                                       R-squared     =  0.0013
                                                       Root MSE      =  .42341

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         job |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0306087    .052415     0.58   0.562    -.0750962    .1363136
       _cons |   .2173913   .0344147     6.32   0.000     .1479875    .2867951
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.12
                                                       Prob > F      =  0.7361
                                                       R-squared     =  0.0004
                                                       Root MSE      =  .41945

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         job |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0169837   .0500723     0.34   0.736    -.0839966     .117964
       _cons |   .2173913   .0344139     6.32   0.000      .147989    .2867936
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    0.12
                                                       Prob > F      =  0.9485
                                                       R-squared     =  0.0006
                                                       Root MSE      =  .42312

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         job |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0111801    .050518     0.22   0.825    -.0891386    .1114988
     hotline |   .0306087   .0521328     0.59   0.559    -.0729168    .1341342
     verdade |   .0169837   .0498038     0.34   0.734    -.0819168    .1158842
       _cons |   .2173913   .0342294     6.35   0.000     .1494184    .2853642
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.12
            Prob > F =    0.9485
.94854289


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.16
                                                       Prob > F      =  0.6927
                                                       R-squared     =  0.0011
                                                       Root MSE      =  .49294

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       agric |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0332505   .0836698    -0.40   0.693    -.2012246    .1347235
       _cons |   .4275362   .0637185     6.71   0.000      .299616    .5554565
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.15
                                                       Prob > F      =  0.6976
                                                       R-squared     =  0.0013
                                                       Root MSE      =  .49351

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       agric |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0355362   .0908608    -0.39   0.698    -.2187744     .147702
       _cons |   .4275362    .063852     6.70   0.000     .2987664     .556306
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.30
                                                       Prob > F      =  0.5887
                                                       R-squared     =  0.0029
                                                       Root MSE      =   .4915

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       agric |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0525362   .0964256    -0.54   0.589     -.246997    .1419245
       _cons |   .4275362   .0638506     6.70   0.000     .2987692    .5563032
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    0.11
                                                       Prob > F      =  0.9536
                                                       R-squared     =  0.0014
                                                       Root MSE      =  .49077

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       agric |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0332505   .0833937    -0.40   0.691    -.1988539    .1323529
     hotline |  -.0355362   .0903717    -0.39   0.695    -.2149965     .143924
     verdade |  -.0525362   .0959087    -0.55   0.585    -.2429918    .1379193
       _cons |   .4275362   .0635083     6.73   0.000     .3014214    .5536511
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.11
            Prob > F =    0.9536
.95364751


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    1.75
                                                       Prob > F      =  0.1916
                                                       R-squared     =  0.0077
                                                       Root MSE      =  .20657

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         com |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.036646    .027692    -1.32   0.192    -.0922399     .018948
       _cons |   .0652174   .0250253     2.61   0.012      .014977    .1154577
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.00
                                                       Prob > F      =  0.9713
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .24683

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         com |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0012174   .0336192    -0.04   0.971     -.069017    .0665822
       _cons |   .0652174   .0250777     2.60   0.013     .0146434    .1157914
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     265
                                                       F(  1,    43) =    0.55
                                                       Prob > F      =  0.4630
                                                       R-squared     =  0.0033
                                                       Root MSE      =  .22417

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         com |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0258473   .0349002    -0.74   0.463    -.0962303    .0445357
       _cons |   .0652174   .0250773     2.60   0.013     .0146441    .1157907
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  3,    93) =    1.04
                                                       Prob > F      =  0.3792
                                                       R-squared     =  0.0058
                                                       Root MSE      =  .21346

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         com |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.036646   .0276008    -1.33   0.188    -.0914556    .0181637
     hotline |  -.0012174   .0334384    -0.04   0.971    -.0676194    .0651847
     verdade |  -.0258473    .034713    -0.74   0.458    -.0947805    .0430859
       _cons |   .0652174   .0249428     2.61   0.010     .0156859    .1147489
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    1.04
            Prob > F =    0.3792
.37918727


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.91
                                                       Prob > F      =  0.3441
                                                       R-squared     =  0.0034
                                                       Root MSE      =  .17613

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         art |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0206211   .0215936    -0.95   0.344     -.063972    .0227298
       _cons |   .0434783   .0186237     2.33   0.024     .0060896    .0808669
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.02
                                                       Prob > F      =  0.8895
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .20095

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         art |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0034783   .0248847    -0.14   0.889     -.053663    .0467065
       _cons |   .0434783   .0186627     2.33   0.025     .0058413    .0811152
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     265
                                                       F(  1,    43) =    0.02
                                                       Prob > F      =  0.8917
                                                       R-squared     =  0.0001
                                                       Root MSE      =   .2087

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         art |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0037658    .027502     0.14   0.892    -.0516972    .0592288
       _cons |   .0434783   .0186624     2.33   0.025     .0058419    .0811146
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  3,    93) =    0.62
                                                       Prob > F      =  0.6019
                                                       R-squared     =  0.0027
                                                       Root MSE      =  .18959

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         art |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0206211   .0215224    -0.96   0.340    -.0633604    .0221182
     hotline |  -.0034783   .0247509    -0.14   0.889    -.0526286     .045672
     verdade |   .0037658   .0273545     0.14   0.891    -.0505547    .0580864
       _cons |   .0434783   .0185623     2.34   0.021     .0066171    .0803394
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.62
            Prob > F =    0.6019
.60193604


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.16
                                                       Prob > F      =  0.6885
                                                       R-squared     =  0.0006
                                                       Root MSE      =  .20011

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         man |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0094824   .0235188     0.40   0.688    -.0377336    .0566984
       _cons |   .0362319   .0175888     2.06   0.045      .000921    .0715428
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.58
                                                       Prob > F      =  0.4511
                                                       R-squared     =  0.0022
                                                       Root MSE      =  .20924

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         man |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0197681   .0259951     0.76   0.451    -.0326561    .0721923
       _cons |   .0362319   .0176256     2.06   0.046     .0006865    .0717773
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     265
                                                       F(  1,    43) =    0.34
                                                       Prob > F      =  0.5646
                                                       R-squared     =  0.0014
                                                       Root MSE      =  .17164

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         man |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0126098   .0217219    -0.58   0.565    -.0564162    .0311965
       _cons |   .0362319   .0176253     2.06   0.046      .000687    .0717768
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  3,    93) =    0.81
                                                       Prob > F      =  0.4902
                                                       R-squared     =  0.0033
                                                       Root MSE      =  .19799

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         man |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0094824   .0234413     0.40   0.687    -.0370674    .0560322
     hotline |   .0197681   .0258553     0.76   0.446    -.0315755    .0711117
     verdade |  -.0126098   .0216054    -0.58   0.561    -.0555139    .0302942
       _cons |   .0362319   .0175308     2.07   0.042     .0014192    .0710446
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.81
            Prob > F =    0.4902
.49019573


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.06
                                                       Prob > F      =  0.8032
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .17641

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       assal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0053002   .0211578     0.25   0.803    -.0371759    .0477763
       _cons |   .0289855   .0167409     1.73   0.089    -.0046233    .0625943
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.05
                                                       Prob > F      =  0.8165
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .16155

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       assal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0049855   .0213534    -0.23   0.817    -.0480488    .0380778
       _cons |   .0289855    .016776     1.73   0.091    -.0048465    .0628175
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     265
                                                       F(  1,    43) =    0.43
                                                       Prob > F      =  0.5148
                                                       R-squared     =  0.0020
                                                       Root MSE      =  .14917

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       assal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0132375   .0201548    -0.66   0.515    -.0538836    .0274086
       _cons |   .0289855   .0167757     1.73   0.091     -.004846     .062817
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  3,    93) =    0.42
                                                       Prob > F      =  0.7367
                                                       R-squared     =  0.0018
                                                       Root MSE      =  .16118

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       assal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0053002   .0210881     0.25   0.802    -.0365766     .047177
     hotline |  -.0049855   .0212386    -0.23   0.815    -.0471612    .0371901
     verdade |  -.0132375   .0200467    -0.66   0.511    -.0530463    .0265713
       _cons |   .0289855   .0166857     1.74   0.086    -.0041491    .0621201
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.42
            Prob > F =    0.7367
.73672454


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.03
                                                       Prob > F      =  0.8620
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .20015

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         tea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0034783   .0199118    -0.17   0.862    -.0434529    .0364964
       _cons |   .0434783   .0146992     2.96   0.005     .0139685    .0729881
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.23
                                                       Prob > F      =  0.6347
                                                       R-squared     =  0.0009
                                                       Root MSE      =   .1919

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         tea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0114783   .0239852    -0.48   0.635    -.0598491    .0368926
       _cons |   .0434783     .01473     2.95   0.005     .0137725     .073184
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     265
                                                       F(  1,    43) =    0.37
                                                       Prob > F      =  0.5487
                                                       R-squared     =  0.0019
                                                       Root MSE      =  .22433

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         tea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0195139   .0322842     0.60   0.549    -.0455934    .0846211
       _cons |   .0434783   .0147297     2.95   0.005     .0137729    .0731836
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  3,    93) =    0.28
                                                       Prob > F      =  0.8370
                                                       R-squared     =  0.0028
                                                       Root MSE      =  .20609

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         tea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0034783   .0198462    -0.18   0.861    -.0428889    .0359324
     hotline |  -.0114783   .0238562    -0.48   0.632     -.058852    .0358955
     verdade |   .0195139    .032111     0.61   0.545    -.0442523      .08328
       _cons |   .0434783   .0146507     2.97   0.004     .0143848    .0725717
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.28
            Prob > F =    0.8370
.83704665


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    5.68
                                                       Prob > F      =  0.0209
                                                       R-squared     =  0.0207
                                                       Root MSE      =  .19063

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      puboff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0556108   .0233305     2.38   0.021     .0087729    .1024486
       _cons |   .0072464   .0070239     1.03   0.307    -.0068546    .0213473
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    2.18
                                                       Prob > F      =  0.1468
                                                       R-squared     =  0.0082
                                                       Root MSE      =  .13652

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      puboff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0247536   .0167508     1.48   0.147    -.0090276    .0585349
       _cons |   .0072464   .0070386     1.03   0.309    -.0069482     .021441
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     265
                                                       F(  1,    43) =    1.22
                                                       Prob > F      =  0.2754
                                                       R-squared     =  0.0045
                                                       Root MSE      =  .12212

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      puboff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0163757   .0148241     1.10   0.275      -.01352    .0462713
       _cons |   .0072464   .0070385     1.03   0.309     -.006948    .0214408
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  3,    93) =    2.49
                                                       Prob > F      =  0.0653
                                                       R-squared     =  0.0141
                                                       Root MSE      =  .17963

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      puboff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0556108   .0232536     2.39   0.019     .0094337    .1017878
     hotline |   .0247536   .0166607     1.49   0.141    -.0083313    .0578386
     verdade |   .0163757   .0147446     1.11   0.270    -.0129042    .0456555
       _cons |   .0072464   .0070007     1.04   0.303    -.0066556    .0211484
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    2.49
            Prob > F =    0.0653
.065313


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    1.47
                                                       Prob > F      =  0.2303
                                                       R-squared     =  0.0059
                                                       Root MSE      =  .19206

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        stud |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0296894   .0244518     1.21   0.230    -.0193996    .0787785
       _cons |   .0217391     .01185     1.83   0.072    -.0020507     .045529
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.21
                                                       Prob > F      =  0.6525
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .16149

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        stud |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0102609   .0226264     0.45   0.652    -.0353696    .0558913
       _cons |   .0217391   .0118748     1.83   0.074    -.0022087     .045687
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     265
                                                       F(  1,    43) =    0.63
                                                       Prob > F      =  0.4327
                                                       R-squared     =  0.0026
                                                       Root MSE      =  .17153

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        stud |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0176309   .0222595     0.79   0.433    -.0272595    .0625214
       _cons |   .0217391   .0118746     1.83   0.074    -.0022084    .0456866
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     565
                                                       F(  3,    93) =    0.58
                                                       Prob > F      =  0.6309
                                                       R-squared     =  0.0036
                                                       Root MSE      =  .18951

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        stud |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0296894   .0243713     1.22   0.226     -.018707    .0780859
     hotline |   .0102609   .0225047     0.46   0.649     -.034429    .0549507
     verdade |   .0176309   .0221401     0.80   0.428    -.0263348    .0615967
       _cons |   .0217391   .0118109     1.84   0.069    -.0017151    .0451933
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.58
            Prob > F =    0.6309
.63094129


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.48
                                                       Prob > F      =  0.4932
                                                       R-squared     =  0.0015
                                                       Root MSE      =   .3451

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         dom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0264596   .0383367    -0.69   0.493    -.1034237    .0505045
       _cons |   .1521739    .029883     5.09   0.000     .0921812    .2121666
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.93
                                                       Prob > F      =  0.3414
                                                       R-squared     =  0.0035
                                                       Root MSE      =  .34036

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         dom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0401739   .0417596    -0.96   0.341    -.1243902    .0440424
       _cons |   .1521739   .0299456     5.08   0.000     .0917828     .212565
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    1.59
                                                       Prob > F      =  0.2143
                                                       R-squared     =  0.0057
                                                       Root MSE      =  .33419

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         dom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0506114   .0401554    -1.26   0.214    -.1315924    .0303696
       _cons |   .1521739    .029945     5.08   0.000     .0917841    .2125637
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    0.59
                                                       Prob > F      =  0.6253
                                                       R-squared     =  0.0031
                                                       Root MSE      =  .32986

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         dom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0264596   .0382102    -0.69   0.490    -.1023375    .0494182
     hotline |  -.0401739   .0415349    -0.97   0.336    -.1226539    .0423061
     verdade |  -.0506114   .0399401    -1.27   0.208    -.1299245    .0287017
       _cons |   .1521739   .0297844     5.11   0.000     .0930279    .2113199
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.59
            Prob > F =    0.6253
.62533235


note: results saved to balance_halves.xml

. 
. foreach i in $demo5 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & pt_dayselec>=50 & pt_days
> elec<=100, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & pt_dayselec>=50 & pt_d
> ayselec<=100, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & pt_dayselec>=50 & pt_d
> ayselec<=100, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0 & pt_dayselec>=50 & pt_dayselec<=1
> 00, cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 14.         xml_tab $list1, below save(balance_halves.xml) append sheet("bal `i' 2")
 15.         estimates clear
 16. 
. }

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.51
                                                       Prob > F      =  0.4796
                                                       R-squared     =  0.0018
                                                       Root MSE      =  .31977

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       house |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0275776   .0387188     0.71   0.480    -.0501535    .1053088
       _cons |   .8695652   .0309693    28.08   0.000     .8073918    .9317386
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.75
                                                       Prob > F      =  0.3914
                                                       R-squared     =  0.0029
                                                       Root MSE      =  .31864

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       house |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .0344348   .0397689     0.87   0.391    -.0457669    .1146365
       _cons |   .8695652   .0310341    28.02   0.000     .8069789    .9321515
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.05
                                                       Prob > F      =  0.8193
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .34334

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       house |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0101902   .0443415    -0.23   0.819    -.0996133    .0792328
       _cons |   .8695652   .0310335    28.02   0.000     .8069803    .9321501
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    0.58
                                                       Prob > F      =  0.6270
                                                       R-squared     =  0.0032
                                                       Root MSE      =  .32158

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       house |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0275776    .038591     0.71   0.477    -.0490565    .1042118
     hotline |   .0344348   .0395549     0.87   0.386    -.0441133    .1129829
     verdade |  -.0101902   .0441037    -0.23   0.818    -.0977715    .0773911
       _cons |   .8695652   .0308671    28.17   0.000     .8082693    .9308611
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.58
            Prob > F =    0.6270
.62695391


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.40
                                                       Prob > F      =  0.5277
                                                       R-squared     =  0.0025
                                                       Root MSE      =  .46593

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        land |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0472878   .0743669    -0.64   0.528    -.1965856    .1020101
       _cons |   .7101449   .0590862    12.02   0.000     .5915243    .8287655
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.99
                                                       Prob > F      =  0.3248
                                                       R-squared     =  0.0084
                                                       Root MSE      =  .47032

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        land |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0861449   .0864833    -1.00   0.325    -.2605551    .0882652
       _cons |   .7101449     .05921    11.99   0.000     .5907365    .8295533
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.49
                                                       Prob > F      =  0.4867
                                                       R-squared     =  0.0044
                                                       Root MSE      =  .46704

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        land |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0617074    .087952    -0.70   0.487    -.2390796    .1156647
       _cons |   .7101449   .0592087    11.99   0.000     .5907391    .8295507
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    0.36
                                                       Prob > F      =  0.7824
                                                       R-squared     =  0.0041
                                                       Root MSE      =  .47354

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        land |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0472878   .0741216    -0.64   0.525    -.1944786     .099903
     hotline |  -.0861449   .0860178    -1.00   0.319    -.2569592    .0846693
     verdade |  -.0617074   .0874805    -0.71   0.482    -.2354264    .1120115
       _cons |   .7101449   .0588913    12.06   0.000     .5931985    .8270914
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.36
            Prob > F =    0.7824
.78238558


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    0.22
                                                       Prob > F      =  0.6431
                                                       R-squared     =  0.0013
                                                       Root MSE      =   .4459

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cattle |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0327122   .0701743    -0.47   0.643     -.173593    .1081686
       _cons |   .2898551   .0530026     5.47   0.000     .1834479    .3962623
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    0.21
                                                       Prob > F      =  0.6483
                                                       R-squared     =  0.0014
                                                       Root MSE      =  .44727

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cattle |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |  -.0338551   .0736965    -0.46   0.648    -.1824782     .114768
       _cons |   .2898551   .0531137     5.46   0.000     .1827412     .396969
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.18
                                                       Prob > F      =  0.6696
                                                       R-squared     =  0.0013
                                                       Root MSE      =  .44763

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cattle |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |  -.0320426   .0745696    -0.43   0.670    -.1824265    .1183413
       _cons |   .2898551   .0531125     5.46   0.000     .1827435    .3969666
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    0.10
                                                       Prob > F      =  0.9597
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .44268

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cattle |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0327122   .0699428    -0.47   0.641    -.1716048    .1061803
     hotline |  -.0338551   .0732998    -0.46   0.645    -.1794139    .1117038
     verdade |  -.0320426   .0741698    -0.43   0.667    -.1793291     .115244
       _cons |   .2898551   .0528278     5.49   0.000     .1849496    .3947605
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.10
            Prob > F =    0.9597
.95970796


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     313
                                                       F(  1,    51) =    1.21
                                                       Prob > F      =  0.2760
                                                       R-squared     =  0.0124
                                                       Root MSE      =   .4696

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         cel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1055901   .0958889     1.10   0.276     -.086915    .2980951
       _cons |   .6086957   .0778055     7.82   0.000     .4524947    .7648966
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     263
                                                       F(  1,    43) =    2.57
                                                       Prob > F      =  0.1162
                                                       R-squared     =  0.0263
                                                       Root MSE      =  .46184

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         cel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .1513043    .094377     1.60   0.116    -.0390251    .3416338
       _cons |   .6086957   .0779684     7.81   0.000     .4514573     .765934
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     266
                                                       F(  1,    43) =    0.59
                                                       Prob > F      =  0.4475
                                                       R-squared     =  0.0068
                                                       Root MSE      =   .4782

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         cel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0788043   .1027893     0.77   0.447      -.12849    .2860987
       _cons |   .6086957   .0779668     7.81   0.000     .4514607    .7659306
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     566
                                                       F(  3,    93) =    0.90
                                                       Prob > F      =  0.4421
                                                       R-squared     =  0.0135
                                                       Root MSE      =  .45993

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         cel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1055901   .0955726     1.10   0.272    -.0841981    .2953783
     hotline |   .1513043    .093869     1.61   0.110    -.0351009    .3377096
     verdade |   .0788043   .1022382     0.77   0.443    -.1242205    .2818292
       _cons |   .6086957   .0775488     7.85   0.000     .4546992    .7626921
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.90
            Prob > F =    0.4421
.44212385


note: results saved to balance_halves.xml

Linear regression                                      Number of obs =     306
                                                       F(  1,    51) =    0.89
                                                       Prob > F      =  0.3505
                                                       R-squared     =  0.0045
                                                       Root MSE      =  111.97

                                    (Std. Err. adjusted for 52 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 expenditure |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |     15.093    16.0191     0.94   0.351    -17.06668    47.25268
       _cons |   94.89572   12.42347     7.64   0.000     69.95456    119.8369
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     255
                                                       F(  1,    43) =    0.32
                                                       Prob > F      =  0.5755
                                                       R-squared     =  0.0023
                                                       Root MSE      =  103.33

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 expenditure |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   9.942855   17.61889     0.56   0.575    -25.58903    45.47474
       _cons |   94.89572   12.44979     7.62   0.000     69.78833    120.0031
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     262
                                                       F(  1,    43) =    1.18
                                                       Prob > F      =  0.2833
                                                       R-squared     =  0.0074
                                                       Root MSE      =  113.11

                                    (Std. Err. adjusted for 44 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 expenditure |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   19.50237   17.94834     1.09   0.283    -16.69392    55.69866
       _cons |   94.89572   12.44913     7.62   0.000     69.78966    120.0018
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     549
                                                       F(  3,    93) =    0.46
                                                       Prob > F      =  0.7080
                                                       R-squared     =  0.0045
                                                       Root MSE      =  107.21

                                    (Std. Err. adjusted for 94 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 expenditure |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |     15.093   15.96698     0.95   0.347    -16.61426    46.80026
     hotline |   9.942855   17.52445     0.57   0.572    -24.85723    44.74294
     verdade |   19.50237   17.85308     1.09   0.277    -15.95031    54.95505
       _cons |   94.89572   12.38306     7.66   0.000     70.30543     119.486
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    93) =    0.46
            Prob > F =    0.7080
.70800281


note: results saved to balance_halves.xml

. 
. foreach i in $demo6 {
  2. 
.         global list1=""
  3. 
.         regress `i' civiceduc if time==0 & hotline==0 & verdade==0 & pt_dayselec>=50 & pt_days
> elec<=100, cluster(ea)
  4.         estimates store `i'_1
  5.         regress `i' hotline if time==0 & civiceduc==0 & verdade==0 & pt_dayselec>=50 & pt_d
> ayselec<=100, cluster(ea)
  6.         estimates store `i'_2
  7.         regress `i' verdade if time==0 & civiceduc==0 & hotline==0 & pt_dayselec>=50 & pt_d
> ayselec<=100, cluster(ea)
  8.         estimates store `i'_3
  9.         regress `i' civiceduc hotline verdade if time==0 & pt_dayselec>=50 & pt_dayselec<=1
> 00, cluster(ea)
 10.         test civiceduc hotline verdade
 11.         scalar define f`i'_1=r(p)
 12.         display f`i'_1
 13. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 14.         xml_tab $list1, below save(balance_halves.xml) append sheet("bal `i' 2")
 15.         estimates clear
 16. 
. }

Linear regression                                      Number of obs =     262
                                                       F(  1,    44) =    0.59
                                                       Prob > F      =  0.4453
                                                       R-squared     =  0.0086
                                                       Root MSE      =   .7869

                                    (Std. Err. adjusted for 45 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
netmean_dist |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1469904   .1908485     0.77   0.445    -.2376396    .5316204
       _cons |   1.136829   .0952259    11.94   0.000      .944914    1.328744
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     209
                                                       F(  1,    36) =    0.25
                                                       Prob > F      =  0.6181
                                                       R-squared     =  0.0069
                                                       Root MSE      =  .68234

                                    (Std. Err. adjusted for 37 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
netmean_dist |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     hotline |   .1139204   .2265224     0.50   0.618    -.3454882    .5733291
       _cons |   1.136829   .0955075    11.90   0.000     .9431309    1.330527
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     241
                                                       F(  1,    40) =    0.14
                                                       Prob > F      =  0.7093
                                                       R-squared     =  0.0026
                                                       Root MSE      =  .68884

                                    (Std. Err. adjusted for 41 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
netmean_dist |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     verdade |   .0701424   .1868196     0.38   0.709    -.3074341    .4477189
       _cons |   1.136829   .0953477    11.92   0.000     .9441243    1.329534
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     474
                                                       F(  3,    82) =    0.25
                                                       Prob > F      =  0.8640
                                                       R-squared     =  0.0049
                                                       Root MSE      =  .80928

                                    (Std. Err. adjusted for 83 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
netmean_dist |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1469904   .1901031     0.77   0.442    -.2311851    .5251659
     hotline |   .1139204   .2249721     0.51   0.614    -.3336208    .5614617
     verdade |   .0701424   .1858522     0.38   0.707    -.2995767    .4398616
       _cons |   1.136829   .0948539    11.99   0.000     .9481345    1.325524
------------------------------------------------------------------------------

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,    82) =    0.25
            Prob > F =    0.8640
.86396612


note: results saved to balance_halves.xml

. 
. global list2=""

. matrix define fpvalue_1=(fsex_1 \ fage_1 \ fhead_1 \ fhousen_1 \ fsingle_1 \ fmarriedunion_1 \
>  fnoschl_1 \ finformalschl_1 \ flit_1 \ fprim5y_1 \ fsec10y_1 \ fchang_1 \ fmacua_1 \ flomue_1
>  \ fchuabo_1 \ fchironga_1 \ fmaconde_1 \ fcathol_1 \ fprotest_1 \ fmuslim_1 \ fjob_1 \ fagric
> _1 \ fcom_1 \ fart_1 \ fman_1 \ fassal_1 \ ftea_1 \ fpuboff_1 \ fstud_1 \ fdom_1 \ fhouse_1 \ 
> fland_1 \ fcattle_1 \ fcel_1 \ fexpenditure_1 \ fnetmean_dist_1)

. matrix rownames fpvalue_1 = "sex" "age" "head" "housen" "single" "marriedunion" "noschl" "info
> rmalschl" "lit" "prim5y" "sec10y" "chang" "macua" "lomue" "chuabo" "chironga" "maconde" "catho
> l" "protest" "muslim" "job" "agric" "com" "art" "man" "assal" "tea" "puboff" "stud" "dom" "hou
> se" "land" "cattle" "cel" "expenditure" "netmean_dist"

. matrix fpvalue= (fpvalue_1)

. global list2="$list2" + " fpvalue"

. xml_tab $list2, save(balance_halves.xml) append sheet("fpvalue demo 2") 


note: results saved to balance_halves.xml

. estimates clear

. 
. ************************************************************************
. *****  TABLE 2 AND OA TABLE 10 (PART): REGRESSIONS OF OPEN LETTER  *****
. ************************************************************************
. 
. global carta="carta"

. 
. global ea="market market_miss"

. global controls="sex age divor school protest relig com tea econfood econmedic chitsua"

. 
. global list1=""

. global list2=""

. 
. foreach i in $carta {
  2. 
.         regress `i' $treat $prov if time==1, cluster(ea)
  3.         estimates store `i'_2_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2_1=r(mean)
  6.         display m_`i'_2_1
  7.         test civiceduc = hotline
  8.         scalar define t_`i'_2_1_1=r(p)
  9.         display t_`i'_2_1_1
 10.         test civiceduc = verdade
 11.         scalar define t_`i'_2_1_2=r(p)
 12.         display t_`i'_2_1_2
 13.         test hotline = verdade
 14.         scalar define t_`i'_2_1_3=r(p)
 15.         display t_`i'_2_1_3
 16.         test civiceduc hotline verdade
 17.         scalar define t_`i'_2_1_4=r(p)
 18.         display t_`i'_2_1_4
 19. 
.         regress `i' $treat $prov if time==1 & lazy==0, cluster(ea)
 20.         estimates store `i'_2_2
 21.         regress `i' $treat $prov if time==1 & lazy==0
 22.         estimates store `i'_2_2a
 23.         
.         regress `i' $treat $prov if time==1 & (lazy==1|control==1), cluster(ea)
 24.         estimates store `i'_2_3
 25.         regress `i' $treat $prov if time==1 & (lazy==1|control==1)
 26.         estimates store `i'_2_3a
 27. 
.         suest `i'_2_2a `i'_2_3a, cluster(ea)
 28.         test [`i'_2_2a_mean]civiceduc=[`i'_2_3a_mean]civiceduc  
 29.         scalar define t_`i'_2_5=r(p)
 30.         display t_`i'_2_5
 31.         test [`i'_2_2a_mean]hotline=[`i'_2_3a_mean]hotline      
 32.         scalar define t_`i'_2_6=r(p)
 33.         display t_`i'_2_6
 34.         test [`i'_2_2a_mean]verdade=[`i'_2_3a_mean]verdade
 35.         scalar define t_`i'_2_7=r(p)
 36.         display t_`i'_2_7
 37. 
.         regress `i' $treat $prov $ea $controls if time==1, cluster(ea)
 38.         estimates store `i'_3_1
 39.         sum `i' if e(sample) & control == 1
 40.         scalar define m_`i'_3_1=r(mean)
 41.         display m_`i'_3_1
 42.         test civiceduc = hotline
 43.         scalar define t_`i'_3_1_1=r(p)
 44.         display t_`i'_3_1_1
 45.         test civiceduc = verdade
 46.         scalar define t_`i'_3_1_2=r(p)
 47.         display t_`i'_3_1_2
 48.         test hotline = verdade
 49.         scalar define t_`i'_3_1_3=r(p)
 50.         display t_`i'_3_1_3
 51.         test civiceduc hotline verdade
 52.         scalar define t_`i'_3_1_4=r(p)
 53.         display t_`i'_3_1_4
 54. 
.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0, cluster(ea)
 55.         estimates store `i'_3_2
 56.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0
 57.         estimates store `i'_3_2a
 58.         
.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1), cluster(ea)
 59.         estimates store `i'_3_3
 60.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1)
 61.         estimates store `i'_3_3a
 62. 
.         suest `i'_3_2a `i'_3_3a, cluster(ea)
 63.         test [`i'_3_2a_mean]civiceduc=[`i'_3_3a_mean]civiceduc  
 64.         scalar define t_`i'_3_5=r(p)
 65.         display t_`i'_3_5
 66.         test [`i'_3_2a_mean]hotline=[`i'_3_3a_mean]hotline      
 67.         scalar define t_`i'_3_6=r(p)
 68.         display t_`i'_3_6
 69.         test [`i'_3_2a_mean]verdade=[`i'_3_3a_mean]verdade
 70.         scalar define t_`i'_3_7=r(p)
 71.         display t_`i'_3_7
 72.         
.         global list1="$list1" + " `i'_2_1" + " `i'_3_1" + " `i'_2_2" + " `i'_3_2" + " `i'_2_3"
>  + " `i'_3_3"
 73.         
.         }

Linear regression                                      Number of obs =    1147
                                                       F(  6,   160) =    2.32
                                                       Prob > F      =  0.0359
                                                       R-squared     =  0.0182
                                                       Root MSE      =   .3815

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       carta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0545028   .0451424     1.21   0.229     -.034649    .1436546
     hotline |  -.0268027   .0344954    -0.78   0.438    -.0949277    .0413222
     verdade |   .0825894   .0480511     1.72   0.088    -.0123068    .1774856
         pr1 |   .0508742   .0533223     0.95   0.341    -.0544321    .1561804
         pr2 |   .0178749   .0446698     0.40   0.690    -.0703435    .1060933
         pr3 |   .0740775   .0474718     1.56   0.121    -.0196748    .1678297
       _cons |   .1169102   .0387829     3.01   0.003     .0403178    .1935026
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       carta |       275    .1527273    .3603802          0          1
.15272727

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    3.45
            Prob > F =    0.0651
.06505617

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.26
            Prob > F =    0.6130
.61297981

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    5.41
            Prob > F =    0.0213
.02129656

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    2.43
            Prob > F =    0.0676
.06764533

Linear regression                                      Number of obs =     973
                                                       F(  6,   160) =    2.11
                                                       Prob > F      =  0.0546
                                                       R-squared     =  0.0191
                                                       Root MSE      =  .37744

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       carta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0434095   .0481307     0.90   0.368     -.051644    .1384629
     hotline |  -.0355599   .0345253    -1.03   0.305    -.1037441    .0326242
     verdade |   .0878337   .0495795     1.77   0.078    -.0100809    .1857483
         pr1 |   .0410921   .0536886     0.77   0.445    -.0649377    .1471219
         pr2 |   .0115255   .0465817     0.25   0.805    -.0804687    .1035198
         pr3 |   .0686394   .0506807     1.35   0.178    -.0314501    .1687289
       _cons |   .1223611   .0395227     3.10   0.002     .0443076    .2004145
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     973
-------------+------------------------------           F(  6,   966) =    3.13
       Model |   2.6794141     6  .446569017           Prob > F      =  0.0048
    Residual |  137.618633   966  .142462353           R-squared     =  0.0191
-------------+------------------------------           Adj R-squared =  0.0130
       Total |  140.298047   972  .144339555           Root MSE      =  .37744

------------------------------------------------------------------------------
       carta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0434095   .0331975     1.31   0.191    -.0217381    .1085571
     hotline |  -.0355599   .0336211    -1.06   0.290    -.1015388     .030419
     verdade |   .0878337   .0341175     2.57   0.010     .0208808    .1547866
         pr1 |   .0410921   .0340945     1.21   0.228    -.0258158    .1079999
         pr2 |   .0115255   .0341394     0.34   0.736    -.0554703    .0785214
         pr3 |   .0686394   .0345055     1.99   0.047      .000925    .1363537
       _cons |   .1223611   .0310678     3.94   0.000     .0613929    .1833292
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     449
                                                       F(  6,   151) =    2.28
                                                       Prob > F      =  0.0393
                                                       R-squared     =  0.0286
                                                       Root MSE      =  .37635

                                   (Std. Err. adjusted for 152 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       carta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1057196   .0695996     1.52   0.131    -.0317951    .2432344
     hotline |   .0072446   .0473647     0.15   0.879    -.0863385    .1008277
     verdade |   .0650704   .0655117     0.99   0.322    -.0643676    .1945085
         pr1 |    .080213   .0523939     1.53   0.128    -.0233068    .1837327
         pr2 |   .1002344   .0489498     2.05   0.042     .0035195    .1969494
         pr3 |   .1431101   .0508303     2.82   0.006     .0426796    .2435406
       _cons |    .071408   .0314612     2.27   0.025     .0092469     .133569
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     449
-------------+------------------------------           F(  6,   442) =    2.17
       Model |   1.8460197     6   .30766995           Prob > F      =  0.0446
    Residual |  62.6038689   442  .141637713           R-squared     =  0.0286
-------------+------------------------------           Adj R-squared =  0.0155
       Total |  64.4498886   448  .143861359           Root MSE      =  .37635

------------------------------------------------------------------------------
       carta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1057196   .0560795     1.89   0.060    -.0044959    .2159351
     hotline |   .0072446   .0526863     0.14   0.891    -.0963023    .1107915
     verdade |   .0650704   .0548548     1.19   0.236    -.0427383    .1728791
         pr1 |    .080213   .0501417     1.60   0.110    -.0183328    .1787588
         pr2 |   .1002344   .0502815     1.99   0.047     .0014138     .199055
         pr3 |   .1431101   .0497113     2.88   0.004     .0454102      .24081
       _cons |    .071408   .0382014     1.87   0.062    -.0036709    .1464869
------------------------------------------------------------------------------

Simultaneous results for carta_2_2a, carta_2_3a

                                                  Number of obs   =       1147

                                       (Std. Err. adjusted for 161 clusters in ea)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
carta_2_2a_mean  |
       civiceduc |   .0434095    .047982     0.90   0.366    -.0506334    .1374524
         hotline |  -.0355599   .0344186    -1.03   0.302    -.1030192    .0318993
         verdade |   .0878337   .0494262     1.78   0.076    -.0090399    .1847073
             pr1 |   .0410921   .0535227     0.77   0.443    -.0638105    .1459946
             pr2 |   .0115255   .0464377     0.25   0.804    -.0794907    .1025418
             pr3 |   .0686394   .0505241     1.36   0.174     -.030386    .1676648
           _cons |   .1223611   .0394005     3.11   0.002     .0451374    .1995847
-----------------+----------------------------------------------------------------
carta_2_2a_lnvar |
           _cons |  -1.948678   .0755512   -25.79   0.000    -2.096755     -1.8006
-----------------+----------------------------------------------------------------
carta_2_3a_mean  |
       civiceduc |   .1057196   .0691191     1.53   0.126    -.0297514    .2411906
         hotline |   .0072446   .0470377     0.15   0.878    -.0849476    .0994369
         verdade |   .0650704   .0650595     1.00   0.317    -.0624439    .1925848
             pr1 |    .080213   .0520322     1.54   0.123    -.0217683    .1821942
             pr2 |   .1002344   .0486119     2.06   0.039     .0049568     .195512
             pr3 |   .1431101   .0504795     2.84   0.005     .0441722     .242048
           _cons |    .071408    .031244     2.29   0.022     .0101708    .1326452
-----------------+----------------------------------------------------------------
carta_2_3a_lnvar |
           _cons |  -1.954483   .0927122   -21.08   0.000    -2.136195    -1.77277
----------------------------------------------------------------------------------

 ( 1)  [carta_2_2a_mean]civiceduc - [carta_2_3a_mean]civiceduc = 0

           chi2(  1) =    0.79
         Prob > chi2 =    0.3732
.37323651

 ( 1)  [carta_2_2a_mean]hotline - [carta_2_3a_mean]hotline = 0

           chi2(  1) =    1.22
         Prob > chi2 =    0.2696
.26960768

 ( 1)  [carta_2_2a_mean]verdade - [carta_2_3a_mean]verdade = 0

           chi2(  1) =    0.16
         Prob > chi2 =    0.6866
.68657168

Linear regression                                      Number of obs =    1127
                                                       F( 19,   160) =    3.11
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0503
                                                       Root MSE      =  .37788

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       carta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0646911   .0440299     1.47   0.144    -.0222636    .1516458
     hotline |  -.0138326   .0326163    -0.42   0.672    -.0782465    .0505814
     verdade |   .0908545   .0485054     1.87   0.063    -.0049388    .1866478
         pr1 |   .0141828   .0538484     0.26   0.793    -.0921625    .1205281
         pr2 |  -.0014315   .0450552    -0.03   0.975    -.0904111    .0875481
         pr3 |   .0792922   .0500501     1.58   0.115    -.0195519    .1781363
      market |     .01662   .0342319     0.49   0.628    -.0509846    .0842245
 market_miss |  -.0665342   .0479806    -1.39   0.167    -.1612912    .0282228
         sex |   .0127138   .0244242     0.52   0.603    -.0355217    .0609493
         age |  -.0015495   .0008533    -1.82   0.071    -.0032347    .0001356
       divor |   -.057598   .1074479    -0.54   0.593     -.269797    .1546011
      school |   .0117689   .0098095     1.20   0.232    -.0076039    .0311418
     protest |  -.0014898   .0302126    -0.05   0.961    -.0611568    .0581772
       relig |   .0299581   .0109865     2.73   0.007     .0082608    .0516553
         com |  -.0321816   .0482544    -0.67   0.506    -.1274793    .0631161
         tea |   .1911103   .0678956     2.81   0.005     .0570231    .3251976
    econfood |   .0043137     .01086     0.40   0.692    -.0171338    .0257612
   econmedic |   -.008647   .0115091    -0.75   0.454    -.0313763    .0140824
     chitsua |   .0383948   .0988936     0.39   0.698    -.1569104       .2337
       _cons |   .0207794   .0691679     0.30   0.764    -.1158203    .1573791
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       carta |       272    .1507353    .3584502          0          1
.15073529

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    3.47
            Prob > F =    0.0645
.06450317

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.21
            Prob > F =    0.6442
.64421524

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    5.48
            Prob > F =    0.0205
.02049382

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    2.73
            Prob > F =    0.0456
.04562875

Linear regression                                      Number of obs =     957
                                                       F( 19,   160) =    2.34
                                                       Prob > F      =  0.0023
                                                       R-squared     =  0.0482
                                                       Root MSE      =  .37421

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       carta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0532275   .0464315     1.15   0.253    -.0384701    .1449252
     hotline |  -.0166906   .0334279    -0.50   0.618    -.0827075    .0493263
     verdade |   .0986482   .0497715     1.98   0.049     .0003545    .1969419
         pr1 |   .0133022   .0541497     0.25   0.806    -.0936381    .1202425
         pr2 |  -.0068134   .0475382    -0.14   0.886    -.1006967    .0870699
         pr3 |   .0758383   .0537432     1.41   0.160    -.0302992    .1819759
      market |    .045831    .034967     1.31   0.192    -.0232255    .1148874
 market_miss |  -.0224156    .053891    -0.42   0.678     -.128845    .0840139
         sex |    .009958   .0284864     0.35   0.727    -.0462998    .0662158
         age |  -.0021392   .0009187    -2.33   0.021    -.0039536   -.0003248
       divor |  -.0171316    .135651    -0.13   0.900    -.2850291    .2507658
      school |   .0099618   .0096035     1.04   0.301    -.0090042    .0289278
     protest |   .0152992   .0330703     0.46   0.644    -.0500115    .0806098
       relig |   .0269125    .011107     2.42   0.017     .0049773    .0488477
         com |  -.0709977   .0459229    -1.55   0.124    -.1616909    .0196955
         tea |    .135173   .0727431     1.86   0.065    -.0084874    .2788334
    econfood |    .006152   .0115714     0.53   0.596    -.0167004    .0290044
   econmedic |   -.009454   .0120364    -0.79   0.433    -.0332246    .0143166
     chitsua |   .0935107   .1161177     0.81   0.422    -.1358103    .3228317
       _cons |   .0329297   .0747946     0.44   0.660    -.1147822    .1806416
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     957
-------------+------------------------------           F( 19,   937) =    2.50
       Model |  6.64384274    19  .349675934           Prob > F      =  0.0004
    Residual |  131.214046   937  .140036336           R-squared     =  0.0482
-------------+------------------------------           Adj R-squared =  0.0289
       Total |  137.857889   956  .144202813           Root MSE      =  .37421

------------------------------------------------------------------------------
       carta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0532275   .0334365     1.59   0.112    -.0123915    .1188466
     hotline |  -.0166906   .0346389    -0.48   0.630    -.0846693    .0512881
     verdade |   .0986482   .0350333     2.82   0.005     .0298955     .167401
         pr1 |   .0133022   .0375556     0.35   0.723    -.0604007     .087005
         pr2 |  -.0068134   .0427166    -0.16   0.873    -.0906447    .0770179
         pr3 |   .0758383   .0411975     1.84   0.066    -.0050117    .1566884
      market |    .045831   .0330134     1.39   0.165    -.0189577    .1106196
 market_miss |  -.0224156   .0690078    -0.32   0.745    -.1578434    .1130123
         sex |    .009958    .026097     0.38   0.703    -.0412574    .0611734
         age |  -.0021392    .001007    -2.12   0.034    -.0041155   -.0001628
       divor |  -.0171316   .1437462    -0.12   0.905    -.2992334    .2649701
      school |   .0099618   .0087946     1.13   0.258    -.0072976    .0272212
     protest |   .0152992    .030517     0.50   0.616    -.0445905    .0751888
       relig |   .0269125   .0128384     2.10   0.036     .0017171    .0521079
         com |  -.0709977   .0575357    -1.23   0.218    -.1839114    .0419161
         tea |    .135173   .0619597     2.18   0.029     .0135773    .2567688
    econfood |    .006152   .0121973     0.50   0.614    -.0177851    .0300891
   econmedic |   -.009454   .0121233    -0.78   0.436    -.0332461     .014338
     chitsua |   .0935107   .1110633     0.84   0.400    -.1244508    .3114723
       _cons |   .0329297     .08039     0.41   0.682    -.1248357    .1906951
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     442
                                                       F( 19,   150) =    2.86
                                                       Prob > F      =  0.0002
                                                       R-squared     =  0.0885
                                                       Root MSE      =  .37059

                                   (Std. Err. adjusted for 151 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       carta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1081925    .067506     1.60   0.111     -.025193     .241578
     hotline |   .0221864    .044147     0.50   0.616    -.0650439    .1094167
     verdade |   .0882931   .0654217     1.35   0.179    -.0409739    .2175601
         pr1 |   .0155261   .0568074     0.27   0.785    -.0967199    .1277721
         pr2 |    .033094   .0571127     0.58   0.563    -.0797552    .1459433
         pr3 |   .1275981   .0651651     1.96   0.052    -.0011619    .2563581
      market |   .0002145   .0507507     0.00   0.997    -.1000641    .1004931
 market_miss |  -.0427455    .075045    -0.57   0.570    -.1910273    .1055363
         sex |  -.0258778   .0353989    -0.73   0.466    -.0958226    .0440671
         age |   .0004781   .0012129     0.39   0.694    -.0019185    .0028747
       divor |  -.1086735   .0508592    -2.14   0.034    -.2091664   -.0081807
      school |   .0311726   .0133166     2.34   0.021     .0048603    .0574849
     protest |  -.0031336   .0492194    -0.06   0.949    -.1003864    .0941193
       relig |   .0256104    .017521     1.46   0.146    -.0090094    .0602302
         com |   .0468149   .0888799     0.53   0.599    -.1288034    .2224331
         tea |   .2150697   .0948817     2.27   0.025     .0275925    .4025469
    econfood |  -.0134072   .0152622    -0.88   0.381    -.0435639    .0167494
   econmedic |  -.0118668   .0175395    -0.68   0.500    -.0465232    .0227895
     chitsua |   .0209675   .1875531     0.11   0.911    -.3496197    .3915547
       _cons |  -.0652801   .0984803    -0.66   0.508    -.2598679    .1293077
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     442
-------------+------------------------------           F( 19,   422) =    2.16
       Model |  5.62857275    19  .296240671           Prob > F      =  0.0034
    Residual |  57.9574001   422  .137339811           R-squared     =  0.0885
-------------+------------------------------           Adj R-squared =  0.0475
       Total |  63.5859729   441  .144185879           Root MSE      =  .37059

------------------------------------------------------------------------------
       carta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1081925   .0570005     1.90   0.058    -.0038478    .2202328
     hotline |   .0221864   .0552808     0.40   0.688    -.0864737    .1308465
     verdade |   .0882931   .0563909     1.57   0.118     -.022549    .1991351
         pr1 |   .0155261   .0567148     0.27   0.784    -.0959525    .1270047
         pr2 |    .033094   .0625264     0.53   0.597    -.0898079    .1559959
         pr3 |   .1275981   .0599035     2.13   0.034     .0098516    .2453445
      market |   .0002145   .0519512     0.00   0.997    -.1019009    .1023299
 market_miss |  -.0427455   .1073775    -0.40   0.691    -.2538069    .1683159
         sex |  -.0258778   .0385666    -0.67   0.503    -.1016844    .0499288
         age |   .0004781   .0014546     0.33   0.743    -.0023812    .0033373
       divor |  -.1086735   .2180304    -0.50   0.618    -.5372344    .3198873
      school |   .0311726   .0130467     2.39   0.017      .005528    .0568172
     protest |  -.0031336   .0459673    -0.07   0.946     -.093487    .0872198
       relig |   .0256104   .0180828     1.42   0.157    -.0099332     .061154
         com |   .0468149   .0938575     0.50   0.618    -.1376716    .2313014
         tea |   .2150697   .0809796     2.66   0.008      .055896    .3742433
    econfood |  -.0134072   .0175991    -0.76   0.447    -.0480001    .0211856
   econmedic |  -.0118668   .0178607    -0.66   0.507    -.0469739    .0232403
     chitsua |   .0209675   .1723114     0.12   0.903    -.3177281    .3596631
       _cons |  -.0652801   .1146434    -0.57   0.569    -.2906233    .1600631
------------------------------------------------------------------------------

Simultaneous results for carta_3_2a, carta_3_3a

                                                  Number of obs   =       1127

                                       (Std. Err. adjusted for 161 clusters in ea)
----------------------------------------------------------------------------------
                 |               Robust
                 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
carta_3_2a_mean  |
       civiceduc |   .0532275   .0459678     1.16   0.247    -.0368677    .1433228
         hotline |  -.0166906   .0330941    -0.50   0.614    -.0815538    .0481726
         verdade |   .0986482   .0492744     2.00   0.045     .0020722    .1952242
             pr1 |   .0133022   .0536089     0.25   0.804    -.0917693    .1183737
             pr2 |  -.0068134   .0470635    -0.14   0.885    -.0990561    .0854293
             pr3 |   .0758383   .0532065     1.43   0.154    -.0284444    .1801211
          market |    .045831   .0346178     1.32   0.186    -.0220187    .1136806
     market_miss |  -.0224156   .0533528    -0.42   0.674    -.1269851     .082154
             sex |    .009958   .0282019     0.35   0.724    -.0453167    .0652327
             age |  -.0021392   .0009095    -2.35   0.019    -.0039218   -.0003565
           divor |  -.0171316   .1342963    -0.13   0.898    -.2803475    .2460842
          school |   .0099618   .0095076     1.05   0.295    -.0086727    .0285964
         protest |   .0152992     .03274     0.47   0.640    -.0488701    .0794685
           relig |   .0269125    .010996     2.45   0.014     .0053606    .0484643
             com |  -.0709977   .0454643    -1.56   0.118     -.160106    .0181107
             tea |    .135173   .0720166     1.88   0.061    -.0059769    .2763229
        econfood |    .006152   .0114558     0.54   0.591     -.016301     .028605
       econmedic |   -.009454   .0119162    -0.79   0.428    -.0328092    .0139012
         chitsua |   .0935107    .114958     0.81   0.416    -.1318029    .3188243
           _cons |   .0329297   .0740476     0.44   0.657    -.1122009    .1780603
-----------------+----------------------------------------------------------------
carta_3_2a_lnvar |
           _cons |  -1.965853   .0727716   -27.01   0.000    -2.108483   -1.823224
-----------------+----------------------------------------------------------------
carta_3_3a_mean  |
       civiceduc |   .1081925   .0660221     1.64   0.101    -.0212085    .2375935
         hotline |   .0221864   .0431766     0.51   0.607    -.0624382    .1068109
         verdade |   .0882931   .0639836     1.38   0.168    -.0371124    .2136986
             pr1 |   .0155261   .0555587     0.28   0.780    -.0933669    .1244191
             pr2 |    .033094   .0558573     0.59   0.554    -.0763842    .1425723
             pr3 |   .1275981   .0637326     2.00   0.045     .0026844    .2525117
          market |   .0002145   .0496351     0.00   0.997    -.0970686    .0974976
     market_miss |  -.0427455   .0733954    -0.58   0.560    -.1865978    .1011068
             sex |  -.0258778   .0346208    -0.75   0.455    -.0937332    .0419777
             age |   .0004781   .0011863     0.40   0.687    -.0018469    .0028031
           divor |  -.1086735   .0497412    -2.18   0.029    -.2061645   -.0111826
          school |   .0311726   .0130239     2.39   0.017     .0056463    .0566989
         protest |  -.0031336   .0481375    -0.07   0.948    -.0974813    .0912141
           relig |   .0256104   .0171358     1.49   0.135    -.0079752    .0591961
             com |   .0468149   .0869262     0.54   0.590    -.1235573     .217187
             tea |   .2150697    .092796     2.32   0.020     .0331928    .3969465
        econfood |  -.0134072   .0149267    -0.90   0.369    -.0426631    .0158486
       econmedic |  -.0118668   .0171539    -0.69   0.489    -.0454879    .0217543
         chitsua |   .0209675   .1834304     0.11   0.909    -.3385495    .3804845
           _cons |  -.0652801   .0963156    -0.68   0.498    -.2540552     .123495
-----------------+----------------------------------------------------------------
carta_3_3a_lnvar |
           _cons |  -1.985297   .0887466   -22.37   0.000    -2.159237   -1.811357
----------------------------------------------------------------------------------

 ( 1)  [carta_3_2a_mean]civiceduc - [carta_3_3a_mean]civiceduc = 0

           chi2(  1) =    0.67
         Prob > chi2 =    0.4118
.41176845

 ( 1)  [carta_3_2a_mean]hotline - [carta_3_3a_mean]hotline = 0

           chi2(  1) =    0.99
         Prob > chi2 =    0.3201
.32009333

 ( 1)  [carta_3_2a_mean]verdade - [carta_3_3a_mean]verdade = 0

           chi2(  1) =    0.03
         Prob > chi2 =    0.8532
.85320216

. 
. matrix define means=(m_carta_2_1, m_carta_3_1 \ t_carta_2_1_1, t_carta_3_1_1 \ t_carta_2_1_2, 
> t_carta_3_1_2 \ t_carta_2_1_3, t_carta_3_1_3 \ t_carta_2_1_4, t_carta_3_1_4 \ t_carta_2_5, t_c
> arta_3_5 \ t_carta_2_6, t_carta_3_6 \ t_carta_2_7, t_carta_3_7)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_turnoutcarta.xml") append sheet("
> carta") 


note: results saved to outputregs_turnoutcarta.xml

. xml_tab $list2, save("outputregs_turnoutcarta.xml") append sheet("carta stats") 


note: results saved to outputregs_turnoutcarta.xml

. estimates clear

. 
. ***********************************************************
. *****  OA TABLE 12: REGRESSIONS OF INDIVIDUAL VOTING  *****
. ***********************************************************
. 
. global voting1="guebas2"

. global voting2="dlakhama2"

. global voting3="simango2"

. global voting4="frelimo2"

. global voting5="renamo2"

. 
. global ea="post post_miss health health_miss police police_miss"

. global controls="sex age single divor norelig protest com prof comform econfood house oven lch
> ang llomue lchuabo lchitewe lronga chitsua living"

. 
. global list1=""

. global list2=""

. 
. foreach i in $voting1 {
  2. 
.         regress `i' $treat $prov if time==1, cluster(ea)
  3.         estimates store `i'_2_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2_1=r(mean)
  6.         display m_`i'_2_1
  7.         test civiceduc = hotline
  8.         scalar define t_`i'_2_1_1=r(p)
  9.         display t_`i'_2_1_1
 10.         test civiceduc = verdade
 11.         scalar define t_`i'_2_1_2=r(p)
 12.         display t_`i'_2_1_2
 13.         test hotline = verdade
 14.         scalar define t_`i'_2_1_3=r(p)
 15.         display t_`i'_2_1_3
 16.         test civiceduc hotline verdade
 17.         scalar define t_`i'_2_1_4=r(p)
 18.         display t_`i'_2_1_4
 19. 
.         regress `i' $treat $prov if time==1 & lazy==0, cluster(ea)
 20.         estimates store `i'_2_2
 21.         regress `i' $treat $prov if time==1 & lazy==0
 22.         estimates store `i'_2_2a
 23.         
.         regress `i' $treat $prov if time==1 & (lazy==1|control==1), cluster(ea)
 24.         estimates store `i'_2_3
 25.         regress `i' $treat $prov if time==1 & (lazy==1|control==1)
 26.         estimates store `i'_2_3a
 27. 
.         suest `i'_2_2a `i'_2_3a, cluster(ea)
 28.         test [`i'_2_2a_mean]civiceduc=[`i'_2_3a_mean]civiceduc  
 29.         scalar define t_`i'_2_5=r(p)
 30.         display t_`i'_2_5
 31.         test [`i'_2_2a_mean]hotline=[`i'_2_3a_mean]hotline      
 32.         scalar define t_`i'_2_6=r(p)
 33.         display t_`i'_2_6
 34.         test [`i'_2_2a_mean]verdade=[`i'_2_3a_mean]verdade
 35.         scalar define t_`i'_2_7=r(p)
 36.         display t_`i'_2_7
 37. 
.         regress `i' $treat $prov $ea $controls if time==1, cluster(ea)
 38.         estimates store `i'_3_1
 39.         sum `i' if e(sample) & control == 1
 40.         scalar define m_`i'_3_1=r(mean)
 41.         display m_`i'_3_1
 42.         test civiceduc = hotline
 43.         scalar define t_`i'_3_1_1=r(p)
 44.         display t_`i'_3_1_1
 45.         test civiceduc = verdade
 46.         scalar define t_`i'_3_1_2=r(p)
 47.         display t_`i'_3_1_2
 48.         test hotline = verdade
 49.         scalar define t_`i'_3_1_3=r(p)
 50.         display t_`i'_3_1_3
 51.         test civiceduc hotline verdade
 52.         scalar define t_`i'_3_1_4=r(p)
 53.         display t_`i'_3_1_4
 54. 
.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0, cluster(ea)
 55.         estimates store `i'_3_2
 56.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0
 57.         estimates store `i'_3_2a
 58.         
.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1), cluster(ea)
 59.         estimates store `i'_3_3
 60.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1)
 61.         estimates store `i'_3_3a
 62. 
.         suest `i'_3_2a `i'_3_3a, cluster(ea)
 63.         test [`i'_3_2a_mean]civiceduc=[`i'_3_3a_mean]civiceduc  
 64.         scalar define t_`i'_3_5=r(p)
 65.         display t_`i'_3_5
 66.         test [`i'_3_2a_mean]hotline=[`i'_3_3a_mean]hotline      
 67.         scalar define t_`i'_3_6=r(p)
 68.         display t_`i'_3_6
 69.         test [`i'_3_2a_mean]verdade=[`i'_3_3a_mean]verdade
 70.         scalar define t_`i'_3_7=r(p)
 71.         display t_`i'_3_7
 72. 
.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1, cluster(ea)
 73.         estimates store `i'_4_1
 74.         sum `i' if e(sample) & control == 1
 75.         scalar define m_`i'_4_1=r(mean)
 76.         display m_`i'_4_1
 77.         test civiceduc = hotline
 78.         scalar define t_`i'_4_1_1=r(p)
 79.         display t_`i'_4_1_1
 80.         test civiceduc = verdade
 81.         scalar define t_`i'_4_1_2=r(p)
 82.         display t_`i'_4_1_2
 83.         test hotline = verdade
 84.         scalar define t_`i'_4_1_3=r(p)
 85.         display t_`i'_4_1_3
 86.         test civiceduc hotline verdade
 87.         scalar define t_`i'_4_1_4=r(p)
 88.         display t_`i'_4_1_4
 89. 
.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & lazy==0, cluster
> (ea)
 90.         estimates store `i'_4_2
 91.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & lazy==0
 92.         estimates store `i'_4_2a
 93.         
.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & (lazy==1|control
> ==1), cluster(ea)
 94.         estimates store `i'_4_3
 95.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & (lazy==1|cont
> rol==1)
 96.         estimates store `i'_4_3a
 97. 
.         suest `i'_4_2a `i'_4_3a, cluster(ea)
 98.         test [`i'_4_2a_mean]civiceduc=[`i'_4_3a_mean]civiceduc  
 99.         scalar define t_`i'_4_5=r(p)
100.         display t_`i'_4_5
101.         test [`i'_4_2a_mean]hotline=[`i'_4_3a_mean]hotline      
102.         scalar define t_`i'_4_6=r(p)
103.         display t_`i'_4_6
104.         test [`i'_4_2a_mean]verdade=[`i'_4_3a_mean]verdade
105.         scalar define t_`i'_4_7=r(p)
106.         display t_`i'_4_7
107.         
.         global list1="$list1" + " `i'_2_1" + " `i'_3_1" + " `i'_4_1" + " `i'_2_2" + " `i'_3_2"
>  + " `i'_4_2"  + " `i'_2_3" + " `i'_3_3" + " `i'_4_3"
108.         
.         }

Linear regression                                      Number of obs =    1031
                                                       F(  6,   160) =    4.29
                                                       Prob > F      =  0.0005
                                                       R-squared     =  0.0411
                                                       Root MSE      =  .35487

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0415883   .0355987     1.17   0.244    -.0287156    .1118922
     hotline |   .0481053     .03188     1.51   0.133    -.0148547    .1110652
     verdade |   .0078678   .0381737     0.21   0.837    -.0675215    .0832571
         pr1 |  -.1671694   .0378369    -4.42   0.000    -.2418936   -.0924453
         pr2 |  -.0688101   .0271627    -2.53   0.012    -.1224537   -.0151665
         pr3 |  -.0028081   .0255954    -0.11   0.913    -.0533565    .0477402
       _cons |   .8807319   .0280348    31.42   0.000      .825366    .9360978
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     guebas2 |       249    .8192771    .3855634          0          1
.81927711

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.05
            Prob > F =    0.8297
.82972699

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.85
            Prob > F =    0.3576
.35762112

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    1.50
            Prob > F =    0.2226
.22260214

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.07
            Prob > F =    0.3624
.36237525

Linear regression                                      Number of obs =     872
                                                       F(  6,   160) =    4.75
                                                       Prob > F      =  0.0002
                                                       R-squared     =  0.0436
                                                       Root MSE      =  .35838

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .042957   .0359275     1.20   0.234    -.0279963    .1139102
     hotline |   .0594177   .0320643     1.85   0.066     -.003906    .1227415
     verdade |  -.0106101   .0404863    -0.26   0.794    -.0905665    .0693463
         pr1 |  -.1710025   .0386406    -4.43   0.000     -.247314    -.094691
         pr2 |  -.0948836   .0285805    -3.32   0.001    -.1513273   -.0384398
         pr3 |  -.0174245   .0275936    -0.63   0.529    -.0719192    .0370702
       _cons |   .8914888   .0277142    32.17   0.000     .8367559    .9462216
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     872
-------------+------------------------------           F(  6,   865) =    6.57
       Model |   5.0644607     6  .844076783           Prob > F      =  0.0000
    Residual |   111.09609   865  .128434786           R-squared     =  0.0436
-------------+------------------------------           Adj R-squared =  0.0370
       Total |   116.16055   871  .133364581           Root MSE      =  .35838

------------------------------------------------------------------------------
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .042957   .0331614     1.30   0.196    -.0221292    .1080432
     hotline |   .0594177    .033817     1.76   0.079    -.0069553    .1257908
     verdade |  -.0106101   .0341602    -0.31   0.756    -.0776566    .0564365
         pr1 |  -.1710025   .0335718    -5.09   0.000    -.2368943   -.1051107
         pr2 |  -.0948836    .034942    -2.72   0.007    -.1634646   -.0263026
         pr3 |  -.0174245   .0343118    -0.51   0.612    -.0847686    .0499196
       _cons |   .8914888   .0309592    28.80   0.000     .8307249    .9522526
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     408
                                                       F(  6,   144) =    2.24
                                                       Prob > F      =  0.0426
                                                       R-squared     =  0.0287
                                                       Root MSE      =  .36607

                                   (Std. Err. adjusted for 145 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0373098   .0617394     0.60   0.547    -.0847227    .1593423
     hotline |    .008584   .0515745     0.17   0.868    -.0933568    .1105248
     verdade |   .0821384   .0465685     1.76   0.080    -.0099078    .1741846
         pr1 |  -.0909587   .0575597    -1.58   0.116    -.2047297    .0228123
         pr2 |  -.0791657   .0485647    -1.63   0.105    -.1751575    .0168261
         pr3 |   .0431958   .0398074     1.09   0.280    -.0354864    .1218781
       _cons |   .8507717   .0317204    26.82   0.000     .7880738    .9134695
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     408
-------------+------------------------------           F(  6,   401) =    1.97
       Model |  1.58772268     6  .264620447           Prob > F      =  0.0682
    Residual |  53.7358067   401  .134004506           R-squared     =  0.0287
-------------+------------------------------           Adj R-squared =  0.0142
       Total |  55.3235294   407  .135930048           Root MSE      =  .36607

------------------------------------------------------------------------------
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0373098   .0558921     0.67   0.505    -.0725683    .1471878
     hotline |    .008584   .0539634     0.16   0.874    -.0975024    .1146705
     verdade |   .0821384   .0567807     1.45   0.149    -.0294866    .1937634
         pr1 |  -.0909587    .050872    -1.79   0.075    -.1909679    .0090505
         pr2 |  -.0791657   .0525763    -1.51   0.133    -.1825253    .0241939
         pr3 |   .0431958   .0498601     0.87   0.387    -.0548241    .1412157
       _cons |   .8507717   .0389186    21.86   0.000     .7742618    .9272816
------------------------------------------------------------------------------

Simultaneous results for guebas2_2_2a, guebas2_2_3a

                                                  Number of obs   =       1031

                                         (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------------
                   |               Robust
                   |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
guebas2_2_2a_mean  |
         civiceduc |    .042957   .0358035     1.20   0.230    -.0272166    .1131306
           hotline |   .0594177   .0319536     1.86   0.063    -.0032102    .1220457
           verdade |  -.0106101   .0403466    -0.26   0.793    -.0896879    .0684678
               pr1 |  -.1710025   .0385073    -4.44   0.000    -.2464755   -.0955295
               pr2 |  -.0948836   .0284819    -3.33   0.001    -.1507071     -.03906
               pr3 |  -.0174245   .0274984    -0.63   0.526    -.0713205    .0364714
             _cons |   .8914888   .0276186    32.28   0.000     .8373573    .9456202
-------------------+----------------------------------------------------------------
guebas2_2_2a_lnvar |
             _cons |  -2.052334   .0689582   -29.76   0.000     -2.18749   -1.917178
-------------------+----------------------------------------------------------------
guebas2_2_3a_mean  |
         civiceduc |   .0373098   .0612615     0.61   0.543    -.0827605      .15738
           hotline |    .008584   .0511752     0.17   0.867    -.0917176    .1088857
           verdade |   .0821384    .046208     1.78   0.075    -.0084277    .1727045
               pr1 |  -.0909587   .0571141    -1.59   0.111    -.2029003    .0209829
               pr2 |  -.0791657   .0481888    -1.64   0.100     -.173614    .0152826
               pr3 |   .0431958   .0394992     1.09   0.274    -.0342212    .1206129
             _cons |   .8507717   .0314749    27.03   0.000      .789082    .9124613
-------------------+----------------------------------------------------------------
guebas2_2_3a_lnvar |
             _cons |  -2.009882   .0895252   -22.45   0.000    -2.185348   -1.834416
------------------------------------------------------------------------------------

 ( 1)  [guebas2_2_2a_mean]civiceduc - [guebas2_2_3a_mean]civiceduc = 0

           chi2(  1) =    0.01
         Prob > chi2 =    0.9240
.92402177

 ( 1)  [guebas2_2_2a_mean]hotline - [guebas2_2_3a_mean]hotline = 0

           chi2(  1) =    1.06
         Prob > chi2 =    0.3024
.30241273

 ( 1)  [guebas2_2_2a_mean]verdade - [guebas2_2_3a_mean]verdade = 0

           chi2(  1) =    4.34
         Prob > chi2 =    0.0372
.0372407

Linear regression                                      Number of obs =    1017
                                                       F( 31,   160) =    2.66
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0787
                                                       Root MSE      =  .35053

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0601058   .0340928     1.76   0.080    -.0072242    .1274357
     hotline |   .0585097   .0306983     1.91   0.058    -.0021164    .1191357
     verdade |   .0287733   .0377928     0.76   0.448    -.0458638    .1034103
         pr1 |  -.1762864    .060682    -2.91   0.004    -.2961275   -.0564454
         pr2 |  -.0990556   .0709168    -1.40   0.164    -.2391093     .040998
         pr3 |  -.0662629   .0790465    -0.84   0.403    -.2223719    .0898461
        post |    .023363   .0478249     0.49   0.626    -.0710864    .1178123
   post_miss |     .03576   .0569324     0.63   0.531    -.0766759    .1481959
      health |   .0317139    .023739     1.34   0.183    -.0151683    .0785961
 health_miss |   .1099779   .0572971     1.92   0.057    -.0031782    .2231339
      police |  -.0474094   .0396548    -1.20   0.234    -.1257237    .0309049
 police_miss |  -.2477793   .1422643    -1.74   0.083    -.5287372    .0331787
         sex |   .0065786   .0205902     0.32   0.750    -.0340851    .0472423
         age |  -.0011761   .0010653    -1.10   0.271      -.00328    .0009278
      single |  -.0481667   .0315154    -1.53   0.128    -.1104064     .014073
       divor |  -.0767533   .1501785    -0.51   0.610     -.373341    .2198345
     norelig |  -.0344814   .0616635    -0.56   0.577    -.1562607    .0872979
     protest |  -.0035133   .0307153    -0.11   0.909     -.064173    .0571464
         com |   -.025697   .0504971    -0.51   0.612    -.1254239    .0740298
        prof |   .0189291   .0849466     0.22   0.824    -.1488321    .1866903
     comform |  -.1706319   .1240099    -1.38   0.171    -.4155393    .0742754
    econfood |  -.0143605   .0104411    -1.38   0.171    -.0349806    .0062596
       house |   .0485337   .0386734     1.25   0.211    -.0278424    .1249099
        oven |   .0483514   .0442761     1.09   0.276    -.0390896    .1357925
      lchang |   .0257677   .0703652     0.37   0.715    -.1131967    .1647322
      llomue |   .0552249   .0591725     0.93   0.352     -.061635    .1720848
     lchuabo |  -.0054965   .0580419    -0.09   0.925    -.1201236    .1091306
    lchitewe |  -.1698565   .1474987    -1.15   0.251    -.4611519    .1214388
      lronga |  -.0620978   .0431931    -1.44   0.152       -.1474    .0232043
     chitsua |   .0787444   .0810933     0.97   0.333    -.0814069    .2388956
      living |   .0357128   .0122109     2.92   0.004     .0115974    .0598281
       _cons |   .7809044   .0600739    13.00   0.000     .6622644    .8995445
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     guebas2 |       247    .8218623    .3834055          0          1
.82186235

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.00
            Prob > F =    0.9571
.95710833

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.75
            Prob > F =    0.3890
.38903414

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.81
            Prob > F =    0.3707
.37073935

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.53
            Prob > F =    0.2092
.20923303

Linear regression                                      Number of obs =     862
                                                       F( 31,   160) =    2.67
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0831
                                                       Root MSE      =  .35464

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .059298   .0323262     1.83   0.068     -.004543     .123139
     hotline |   .0596189   .0318771     1.87   0.063    -.0033352    .1225731
     verdade |   .0054802   .0403973     0.14   0.892    -.0743004    .0852609
         pr1 |  -.1690439   .0638395    -2.65   0.009    -.2951207   -.0429671
         pr2 |   -.158066    .081134    -1.95   0.053    -.3182977    .0021658
         pr3 |  -.1193887   .0921235    -1.30   0.197    -.3013235    .0625461
        post |   .0091098   .0550459     0.17   0.869    -.0996004    .1178201
   post_miss |   .0146442   .0560057     0.26   0.794    -.0959616    .1252499
      health |   .0404902    .024929     1.62   0.106    -.0087422    .0897226
 health_miss |   .0994903   .0701483     1.42   0.158    -.0390457    .2380262
      police |  -.0451949   .0468031    -0.97   0.336    -.1376264    .0472365
 police_miss |  -.1699364   .1314128    -1.29   0.198    -.4294637    .0895909
         sex |   .0247459   .0235143     1.05   0.294    -.0216924    .0711843
         age |  -.0012997   .0011494    -1.13   0.260    -.0035697    .0009703
      single |  -.0683842   .0358092    -1.91   0.058    -.1391039    .0023355
       divor |   .0468971   .1347145     0.35   0.728    -.2191508     .312945
     norelig |  -.0370834   .0651106    -0.57   0.570    -.1656704    .0915035
     protest |   .0047305   .0342946     0.14   0.890     -.062998     .072459
         com |   -.000843    .053525    -0.02   0.987    -.1065495    .1048636
        prof |   .0825771   .0768342     1.07   0.284    -.0691629    .2343171
     comform |  -.0585521   .1183707    -0.49   0.622    -.2923226    .1752184
    econfood |   -.016635   .0110358    -1.51   0.134    -.0384296    .0051597
       house |   .0597972   .0437838     1.37   0.174    -.0266714    .1462658
        oven |   .0482023   .0483716     1.00   0.321    -.0473268    .1437314
      lchang |   .0651647   .0809592     0.80   0.422    -.0947217    .2250511
      llomue |   .0588772   .0630229     0.93   0.352    -.0655868    .1833412
     lchuabo |   -.027761   .0609465    -0.46   0.649    -.1481244    .0926024
    lchitewe |  -.0413856   .1729028    -0.24   0.811    -.3828516    .3000805
      lronga |  -.0670287   .0485276    -1.38   0.169     -.162866    .0288087
     chitsua |    .078869   .1018793     0.77   0.440    -.1223325    .2800705
      living |   .0390326   .0130153     3.00   0.003     .0133286    .0647365
       _cons |   .7622723   .0649294    11.74   0.000     .6340432    .8905015
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     862
-------------+------------------------------           F( 31,   830) =    2.43
       Model |  9.46650202    31  .305371033           Prob > F      =  0.0000
    Residual |  104.390807   830  .125772056           R-squared     =  0.0831
-------------+------------------------------           Adj R-squared =  0.0489
       Total |  113.857309   861  .132238454           Root MSE      =  .35464

------------------------------------------------------------------------------
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .059298    .035116     1.69   0.092    -.0096286    .1282246
     hotline |   .0596189   .0348336     1.71   0.087    -.0087533    .1279912
     verdade |   .0054802   .0361335     0.15   0.879    -.0654435    .0764039
         pr1 |  -.1690439   .0537009    -3.15   0.002    -.2744494   -.0636383
         pr2 |   -.158066   .0728616    -2.17   0.030    -.3010806   -.0150513
         pr3 |  -.1193887   .0750378    -1.59   0.112    -.2666749    .0278975
        post |   .0091098   .0501108     0.18   0.856     -.089249    .1074687
   post_miss |   .0146442   .0840614     0.17   0.862    -.1503537    .1796421
      health |   .0404902   .0301029     1.35   0.179    -.0185966     .099577
 health_miss |   .0994903   .0979289     1.02   0.310     -.092727    .2917076
      police |  -.0451949   .0371663    -1.22   0.224    -.1181459     .027756
 police_miss |  -.1699364   .1539831    -1.10   0.270    -.4721784    .1323057
         sex |   .0247459   .0260573     0.95   0.343       -.0264    .0758919
         age |  -.0012997   .0010088    -1.29   0.198    -.0032797    .0006803
      single |  -.0683842    .034139    -2.00   0.045     -.135393   -.0013753
       divor |   .0468971   .1372911     0.34   0.733    -.2225814    .3163756
     norelig |  -.0370834   .0637357    -0.58   0.561    -.1621856    .0880188
     protest |   .0047305   .0309549     0.15   0.879    -.0560286    .0654896
         com |   -.000843   .0573503    -0.01   0.988    -.1134116    .1117257
        prof |   .0825771     .10161     0.81   0.417    -.1168658    .2820199
     comform |  -.0585521   .1217427    -0.48   0.631     -.297512    .1804077
    econfood |   -.016635   .0108821    -1.53   0.127    -.0379946    .0047247
       house |   .0597972   .0374954     1.59   0.111    -.0137997    .1333942
        oven |   .0482023   .0483755     1.00   0.319    -.0467505    .1431551
      lchang |   .0651647   .0646552     1.01   0.314    -.0617422    .1920716
      llomue |   .0588772    .052452     1.12   0.262     -.044077    .1618314
     lchuabo |   -.027761   .0496741    -0.56   0.576    -.1252627    .0697407
    lchitewe |  -.0413856    .138914    -0.30   0.766    -.3140496    .2312784
      lronga |  -.0670287   .0521657    -1.28   0.199    -.1694209    .0353635
     chitsua |    .078869   .1238716     0.64   0.524    -.1642694    .3220074
      living |   .0390326   .0122079     3.20   0.001     .0150706    .0629946
       _cons |   .7622723   .0661259    11.53   0.000     .6324787     .892066
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     402
                                                       F( 31,   142) =    5.49
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1182
                                                       Root MSE      =  .35812

                                   (Std. Err. adjusted for 143 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0464299   .0691964     0.67   0.503    -.0903584    .1832182
     hotline |   .0433084   .0454905     0.95   0.343    -.0466178    .1332346
     verdade |   .1239678   .0460847     2.69   0.008      .032867    .2150685
         pr1 |   .0177098   .0768357     0.23   0.818    -.1341799    .1695994
         pr2 |  -.0307239   .1045004    -0.29   0.769    -.2373015    .1758537
         pr3 |   .0557836   .1079103     0.52   0.606    -.1575347    .2691019
        post |   .0239669   .0585941     0.41   0.683    -.0918625    .1397963
   post_miss |   -.006874   .0485353    -0.14   0.888    -.1028192    .0890712
      health |  -.0023494   .0386592    -0.06   0.952    -.0787713    .0740726
 health_miss |   .2184847   .0603184     3.62   0.000     .0992467    .3377227
      police |  -.0104399   .0520243    -0.20   0.841    -.1132821    .0924022
 police_miss |   -.275475   .2336602    -1.18   0.240    -.7373771    .1864271
         sex |  -.0268324   .0369401    -0.73   0.469    -.0998559    .0461912
         age |  -.0022693   .0022072    -1.03   0.306    -.0066325     .002094
      single |  -.0558566   .0510658    -1.09   0.276     -.156804    .0450908
       divor |  -.2476324   .2963565    -0.84   0.405    -.8334732    .3382083
     norelig |   .0265951   .0997043     0.27   0.790    -.1705016    .2236917
     protest |   .0099197   .0542138     0.18   0.855    -.0972508    .1170902
         com |  -.1123551   .1154411    -0.97   0.332    -.3405603      .11585
        prof |   .0121337   .1046201     0.12   0.908    -.1946805    .2189479
     comform |  -.4778722   .2389858    -2.00   0.047     -.950302   -.0054425
    econfood |  -.0073017   .0125366    -0.58   0.561    -.0320841    .0174807
       house |   .0106863   .0581491     0.18   0.854    -.1042635    .1256361
        oven |  -.0308311   .0863103    -0.36   0.721    -.2014502     .139788
      lchang |  -.0757891   .0981621    -0.77   0.441     -.269837    .1182588
      llomue |  -.0712923   .0684533    -1.04   0.299    -.2066115    .0640269
     lchuabo |  -.1356362   .0773627    -1.75   0.082    -.2885676    .0172951
    lchitewe |  -.4879903   .2368235    -2.06   0.041    -.9561457   -.0198349
      lronga |  -.0237184   .0755995    -0.31   0.754    -.1731643    .1257275
     chitsua |   .1722135   .0838158     2.05   0.042     .0065255    .3379014
      living |   .0471969   .0211403     2.23   0.027     .0054065    .0889873
       _cons |    .832844   .1052713     7.91   0.000     .6247426    1.040945
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     402
-------------+------------------------------           F( 31,   370) =    1.60
       Model |  6.35804006    31  .205098067           Prob > F      =  0.0245
    Residual |  47.4529052   370  .128251095           R-squared     =  0.1182
-------------+------------------------------           Adj R-squared =  0.0443
       Total |  53.8109453   401  .134191883           Root MSE      =  .35812

------------------------------------------------------------------------------
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0464299     .05964     0.78   0.437    -.0708459    .1637058
     hotline |   .0433084   .0560712     0.77   0.440    -.0669497    .1535665
     verdade |   .1239678   .0606865     2.04   0.042      .004634    .2433015
         pr1 |   .0177098   .0869588     0.20   0.839    -.1532856    .1887051
         pr2 |  -.0307239   .1093628    -0.28   0.779    -.2457745    .1843267
         pr3 |   .0557836    .111968     0.50   0.619    -.1643898     .275957
        post |   .0239669   .0666102     0.36   0.719    -.1070153     .154949
   post_miss |   -.006874    .097109    -0.07   0.944    -.1978289    .1840809
      health |  -.0023494   .0473969    -0.05   0.960    -.0955505    .0908518
 health_miss |   .2184847    .171848     1.27   0.204    -.1194365    .5564059
      police |  -.0104399   .0557514    -0.19   0.852    -.1200693    .0991894
 police_miss |   -.275475   .2697564    -1.02   0.308    -.8059229    .2549729
         sex |  -.0268324   .0388656    -0.69   0.490    -.1032576    .0495928
         age |  -.0022693   .0016076    -1.41   0.159    -.0054304    .0008918
      single |  -.0558566   .0485689    -1.15   0.251    -.1513623    .0396491
       divor |  -.2476324   .2119565    -1.17   0.243    -.6644228     .169158
     norelig |   .0265951   .1104124     0.24   0.810    -.1905194    .2437095
     protest |   .0099197   .0498662     0.20   0.842     -.088137    .1079765
         com |  -.1123551   .1016026    -1.11   0.270    -.3121462    .0874359
        prof |   .0121337    .134727     0.09   0.928     -.252793    .2770604
     comform |  -.4778722   .1508559    -3.17   0.002    -.7745148   -.1812297
    econfood |  -.0073017   .0170639    -0.43   0.669    -.0408561    .0262527
       house |   .0106863   .0552847     0.19   0.847    -.0980254     .119398
        oven |  -.0308311   .0750134    -0.41   0.681    -.1783372     .116675
      lchang |  -.0757891   .0962492    -0.79   0.432    -.2650532     .113475
      llomue |  -.0712923   .0857476    -0.83   0.406     -.239906    .0973214
     lchuabo |  -.1356362   .0810329    -1.67   0.095     -.294979    .0237066
    lchitewe |  -.4879903   .1736428    -2.81   0.005    -.8294409   -.1465397
      lronga |  -.0237184   .0745985    -0.32   0.751    -.1704086    .1229717
     chitsua |   .1722135   .2185209     0.79   0.431    -.2574852    .6019121
      living |   .0471969   .0180023     2.62   0.009     .0117973    .0825966
       _cons |    .832844   .0954166     8.73   0.000     .6452171    1.020471
------------------------------------------------------------------------------

Simultaneous results for guebas2_3_2a, guebas2_3_3a

                                                  Number of obs   =       1017

                                         (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------------
                   |               Robust
                   |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
guebas2_3_2a_mean  |
         civiceduc |    .059298   .0317389     1.87   0.062    -.0029091    .1215051
           hotline |   .0596189    .031298     1.90   0.057     -.001724    .1209619
           verdade |   .0054802   .0396634     0.14   0.890    -.0722585     .083219
               pr1 |  -.1690439   .0626797    -2.70   0.007    -.2918939   -.0461938
               pr2 |   -.158066     .07966    -1.98   0.047    -.3141968   -.0019351
               pr3 |  -.1193887   .0904499    -1.32   0.187    -.2966672    .0578897
              post |   .0091098   .0540459     0.17   0.866    -.0968181    .1150378
         post_miss |   .0146442   .0549882     0.27   0.790    -.0931307    .1224191
            health |   .0404902   .0244761     1.65   0.098    -.0074822    .0884625
       health_miss |   .0994903   .0688739     1.44   0.149       -.0355    .2344806
            police |  -.0451949   .0459528    -0.98   0.325    -.1352608    .0448709
       police_miss |  -.1699364   .1290253    -1.32   0.188    -.4228214    .0829486
               sex |   .0247459   .0230871     1.07   0.284    -.0205039    .0699958
               age |  -.0012997   .0011285    -1.15   0.249    -.0035116    .0009122
            single |  -.0683842   .0351587    -1.95   0.052    -.1372939    .0005255
             divor |   .0468971   .1322671     0.35   0.723    -.2123417    .3061359
           norelig |  -.0370834   .0639277    -0.58   0.562    -.1623794    .0882125
           protest |   .0047305   .0336716     0.14   0.888    -.0612646    .0707256
               com |   -.000843   .0525526    -0.02   0.987    -.1038441    .1021582
              prof |   .0825771   .0754383     1.09   0.274    -.0652794    .2304335
           comform |  -.0585521   .1162202    -0.50   0.614    -.2863395    .1692353
          econfood |   -.016635   .0108353    -1.54   0.125    -.0378718    .0046019
             house |   .0597972   .0429883     1.39   0.164    -.0244583    .1440528
              oven |   .0482023   .0474928     1.01   0.310    -.0448819    .1412865
            lchang |   .0651647   .0794884     0.82   0.412    -.0906297     .220959
            llomue |   .0588772   .0618779     0.95   0.341    -.0624013    .1801557
           lchuabo |   -.027761   .0598393    -0.46   0.643    -.1450439    .0895219
          lchitewe |  -.0413856   .1697616    -0.24   0.807    -.3741123    .2913411
            lronga |  -.0670287    .047646    -1.41   0.159    -.1604132    .0263558
           chitsua |    .078869   .1000284     0.79   0.430    -.1171831    .2749211
            living |   .0390326   .0127789     3.05   0.002     .0139865    .0640787
             _cons |   .7622723   .0637498    11.96   0.000      .637325    .8872197
-------------------+----------------------------------------------------------------
guebas2_3_2a_lnvar |
             _cons |  -2.073284   .0661972   -31.32   0.000    -2.203028    -1.94354
-------------------+----------------------------------------------------------------
guebas2_3_3a_mean  |
         civiceduc |   .0464299   .0664418     0.70   0.485    -.0837936    .1766535
           hotline |   .0433084   .0436796     0.99   0.321    -.0423021    .1289189
           verdade |   .1239678   .0442501     2.80   0.005     .0372391    .2106964
               pr1 |   .0177098    .073777     0.24   0.810    -.1268905      .16231
               pr2 |  -.0307239   .1003404    -0.31   0.759    -.2273875    .1659397
               pr3 |   .0557836   .1036146     0.54   0.590    -.1472972    .2588644
              post |   .0239669   .0562615     0.43   0.670    -.0863037    .1342374
         post_miss |   -.006874   .0466032    -0.15   0.883    -.0982146    .0844666
            health |  -.0023494   .0371203    -0.06   0.950    -.0751037     .070405
       health_miss |   .2184847   .0579172     3.77   0.000     .1049691    .3320003
            police |  -.0104399   .0499532    -0.21   0.834    -.1083465    .0874666
       police_miss |   -.275475   .2243585    -1.23   0.220    -.7152096    .1642596
               sex |  -.0268324   .0354695    -0.76   0.449    -.0963514    .0426866
               age |  -.0022693   .0021194    -1.07   0.284    -.0064231    .0018846
            single |  -.0558566   .0490329    -1.14   0.255    -.1519594    .0402462
             divor |  -.2476324   .2845589    -0.87   0.384    -.8053576    .3100928
           norelig |   .0265951   .0957353     0.28   0.781    -.1610426    .2142327
           protest |   .0099197   .0520556     0.19   0.849    -.0921075    .1119469
               com |  -.1123551   .1108455    -1.01   0.311    -.3296083    .1048981
              prof |   .0121337   .1004553     0.12   0.904    -.1847551    .2090225
           comform |  -.4778722   .2294721    -2.08   0.037    -.9276293   -.0281152
          econfood |  -.0073017   .0120375    -0.61   0.544    -.0308947    .0162913
             house |   .0106863   .0558343     0.19   0.848    -.0987469    .1201195
              oven |  -.0308311   .0828744    -0.37   0.710    -.1932619    .1315997
            lchang |  -.0757891   .0942544    -0.80   0.421    -.2605243    .1089461
            llomue |  -.0712923   .0657283    -1.08   0.278    -.2001173    .0575327
           lchuabo |  -.1356362   .0742829    -1.83   0.068    -.2812281    .0099557
          lchitewe |  -.4879903   .2273959    -2.15   0.032    -.9336781   -.0423025
            lronga |  -.0237184     .07259    -0.33   0.744    -.1659922    .1185553
           chitsua |   .1722135   .0804792     2.14   0.032     .0144772    .3299498
            living |   .0471969   .0202987     2.33   0.020     .0074121    .0869817
             _cons |    .832844   .1010805     8.24   0.000     .6347298    1.030958
-------------------+----------------------------------------------------------------
guebas2_3_3a_lnvar |
             _cons |  -2.053765   .0938789   -21.88   0.000    -2.237765   -1.869766
------------------------------------------------------------------------------------

 ( 1)  [guebas2_3_2a_mean]civiceduc - [guebas2_3_3a_mean]civiceduc = 0

           chi2(  1) =    0.05
         Prob > chi2 =    0.8317
.83173786

 ( 1)  [guebas2_3_2a_mean]hotline - [guebas2_3_3a_mean]hotline = 0

           chi2(  1) =    0.15
         Prob > chi2 =    0.7013
.70127616

 ( 1)  [guebas2_3_2a_mean]verdade - [guebas2_3_3a_mean]verdade = 0

           chi2(  1) =    5.49
         Prob > chi2 =    0.0191
.01907306
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_22 omitted because of collinearity

Linear regression                                      Number of obs =    1014
                                                       F( 54,   160) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.1215
                                                       Root MSE      =  .34736

                                     (Std. Err. adjusted for 161 clusters in ea)
--------------------------------------------------------------------------------
               |               Robust
       guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |   .0567551   .0341502     1.66   0.098    -.0106882    .1241984
       hotline |   .0548111   .0309067     1.77   0.078    -.0062266    .1158488
       verdade |   .0229736   .0387403     0.59   0.554    -.0535348     .099482
           pr1 |  -.0787962   .1397265    -0.56   0.574    -.3547424    .1971499
           pr2 |   .0510696   .0944214     0.54   0.589    -.1354033    .2375424
           pr3 |  -.0110972    .136472    -0.08   0.935     -.280616    .2584217
          post |   .0218437   .0496286     0.44   0.660    -.0761679    .1198554
     post_miss |   .0387389   .0552759     0.70   0.484    -.0704256    .1479034
        health |   .0289533   .0241998     1.20   0.233    -.0188389    .0767455
   health_miss |   .1147693   .0548602     2.09   0.038     .0064257    .2231128
        police |  -.0397465   .0408926    -0.97   0.333    -.1205054    .0410124
   police_miss |  -.2709092   .1420456    -1.91   0.058    -.5514353    .0096169
           sex |   .0046915   .0209577     0.22   0.823    -.0366978    .0460808
           age |  -.0007676   .0010463    -0.73   0.464    -.0028339    .0012986
        single |  -.0437455   .0345706    -1.27   0.208     -.112019     .024528
         divor |  -.0520142   .1252076    -0.42   0.678     -.299287    .1952585
       norelig |  -.0299956   .0624274    -0.48   0.632    -.1532837    .0932924
       protest |  -.0028405    .031447    -0.09   0.928    -.0649451    .0592642
           com |  -.0072256   .0486865    -0.15   0.882    -.1033766    .0889254
          prof |  -.0090849   .0826262    -0.11   0.913    -.1722635    .1540937
       comform |  -.1378498   .1280798    -1.08   0.283    -.3907948    .1150951
      econfood |  -.0102098   .0111346    -0.92   0.361    -.0321995      .01178
         house |   .0716466   .0412638     1.74   0.084    -.0098454    .1531386
          oven |   .0429236   .0406259     1.06   0.292    -.0373086    .1231558
        lchang |   .0296565   .0691626     0.43   0.669    -.1069327    .1662458
        llomue |   .0566916   .0610499     0.93   0.354    -.0638759    .1772591
       lchuabo |  -.0033618   .0591223    -0.06   0.955    -.1201225     .113399
      lchitewe |  -.1501446   .1631316    -0.92   0.359    -.4723134    .1720242
        lronga |  -.0702637   .0478659    -1.47   0.144    -.1647942    .0242667
       chitsua |   .1160749    .083789     1.39   0.168    -.0494001    .2815499
        living |   .0356059   .0128904     2.76   0.006     .0101485    .0610632
 _Iinterview_2 |   .0366389   .0472335     0.78   0.439    -.0566425    .1299204
 _Iinterview_3 |  -.0074491   .1134817    -0.07   0.948    -.2315643    .2166661
 _Iinterview_4 |   -.086796   .1004502    -0.86   0.389    -.2851754    .1115833
 _Iinterview_5 |   .0236744   .1005422     0.24   0.814    -.1748866    .2222353
 _Iinterview_6 |  -.1224762   .1180017    -1.04   0.301    -.3555179    .1105654
 _Iinterview_7 |  -.1070622   .0906639    -1.18   0.239    -.2861145    .0719901
 _Iinterview_8 |  -.0086587   .1098299    -0.08   0.937    -.2255618    .2082445
 _Iinterview_9 |  -.2064004   .0952766    -2.17   0.032    -.3945624   -.0182385
_Iinterview_10 |   -.043177   .0789341    -0.55   0.585    -.1990642    .1127101
_Iinterview_11 |  -.2440541   .0924058    -2.64   0.009    -.4265464   -.0615617
_Iinterview_12 |  -.0556553     .10538    -0.53   0.598    -.2637704    .1524598
_Iinterview_13 |  -.1357881    .085557    -1.59   0.114    -.3047548    .0331786
_Iinterview_14 |  -.0918976   .0950371    -0.97   0.335    -.2795866    .0957913
_Iinterview_15 |  -.0109473   .1615922    -0.07   0.946    -.3300761    .3081814
_Iinterview_16 |  -.0864067   .1590865    -0.54   0.588    -.4005868    .2277734
_Iinterview_17 |   -.182162   .1551643    -1.17   0.242    -.4885961    .1242722
_Iinterview_18 |   .0033489    .157624     0.02   0.983     -.307943    .3146409
_Iinterview_19 |  -.1553111    .155478    -1.00   0.319    -.4623648    .1517426
_Iinterview_20 |  -.1439072   .1621451    -0.89   0.376    -.4641279    .1763135
_Iinterview_21 |  -.0212426   .0606242    -0.35   0.727    -.1409695    .0984843
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |   .0812371    .046539     1.75   0.083    -.0106728    .1731471
_Iinterview_24 |   .0694609   .0407254     1.71   0.090    -.0109677    .1498895
_Iinterview_25 |  -.4013755   .0528199    -7.60   0.000    -.5056896   -.2970615
_Iinterview_26 |  -.2612992   .1140557    -2.29   0.023    -.4865479   -.0360505
_Iinterview_27 |   .0024684   .0653751     0.04   0.970    -.1266411    .1315779
_Iinterview_28 |   .1382342   .1769977     0.78   0.436    -.2113188    .4877873
         _cons |   .7457341    .070164    10.63   0.000     .6071672     .884301
--------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     guebas2 |       247    .8218623    .3834055          0          1
.82186235

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.00
            Prob > F =    0.9485
.9485272

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.83
            Prob > F =    0.3624
.36241003

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.92
            Prob > F =    0.3393
.33929514

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.42
            Prob > F =    0.2390
.23900881
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_22 omitted because of collinearity

Linear regression                                      Number of obs =     860
                                                       F( 53,   160) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.1265
                                                       Root MSE      =  .35208

                                     (Std. Err. adjusted for 161 clusters in ea)
--------------------------------------------------------------------------------
               |               Robust
       guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |   .0533135   .0328609     1.62   0.107    -.0115835    .1182106
       hotline |   .0561198   .0319824     1.75   0.081    -.0070423    .1192819
       verdade |   .0033166   .0420159     0.08   0.937    -.0796607    .0862939
           pr1 |  -.0026519   .1388829    -0.02   0.985     -.276932    .2716282
           pr2 |   .0359442   .1161283     0.31   0.757    -.1933979    .2652862
           pr3 |   -.004915   .1662946    -0.03   0.976    -.3333305    .3235005
          post |   .0018885   .0573148     0.03   0.974    -.1113027    .1150796
     post_miss |    .017196   .0548399     0.31   0.754    -.0911074    .1254995
        health |   .0379994    .026038     1.46   0.146    -.0134232     .089422
   health_miss |   .1081563   .0671441     1.61   0.109    -.0244467    .2407594
        police |  -.0349627   .0479322    -0.73   0.467     -.129624    .0596987
   police_miss |  -.2001704   .1313002    -1.52   0.129    -.4594753    .0591345
           sex |    .022706    .024174     0.94   0.349    -.0250352    .0704473
           age |  -.0008463   .0011241    -0.75   0.453    -.0030663    .0013737
        single |  -.0657188   .0392155    -1.68   0.096    -.1431655     .011728
         divor |   .0389569   .1246105     0.31   0.755    -.2071366    .2850504
       norelig |  -.0385605   .0659424    -0.58   0.560    -.1687901    .0916692
       protest |   .0051646   .0351474     0.15   0.883     -.064248    .0745771
           com |   .0214411   .0516788     0.41   0.679    -.0806195    .1235017
          prof |   .0514823   .0745477     0.69   0.491    -.0957421    .1987067
       comform |   -.027576   .1220197    -0.23   0.821    -.2685529     .213401
      econfood |   -.012857   .0119393    -1.08   0.283     -.036436     .010722
         house |   .0706894   .0460103     1.54   0.126    -.0201764    .1615552
          oven |    .046106   .0447735     1.03   0.305    -.0423173    .1345292
        lchang |   .0704687   .0808161     0.87   0.385    -.0891352    .2300726
        llomue |   .0572086   .0664037     0.86   0.390    -.0739323    .1883494
       lchuabo |  -.0268976   .0614542    -0.44   0.662    -.1482637    .0944684
      lchitewe |  -.0144853   .1976436    -0.07   0.942     -.404812    .3758415
        lronga |   -.074492   .0559617    -1.33   0.185    -.1850108    .0360268
       chitsua |   .1239226   .1072337     1.16   0.250    -.0878535    .3356986
        living |   .0387591   .0140943     2.75   0.007     .0109243    .0665939
 _Iinterview_2 |   .0163333   .0580809     0.28   0.779    -.0983707    .1310373
 _Iinterview_3 |  -.0480822   .1293599    -0.37   0.711    -.3035554    .2073909
 _Iinterview_4 |  -.1441309   .1176201    -1.23   0.222    -.3764191    .0881573
 _Iinterview_5 |  -.0194603   .1198511    -0.16   0.871    -.2561544    .2172338
 _Iinterview_6 |  -.2142765   .1374096    -1.56   0.121    -.4856469     .057094
 _Iinterview_7 |  -.1812519   .1112253    -1.63   0.105     -.400911    .0384072
 _Iinterview_8 |  -.0760195   .1299721    -0.58   0.559    -.3327016    .1806626
 _Iinterview_9 |  -.2413516   .1076649    -2.24   0.026    -.4539791   -.0287241
_Iinterview_10 |  -.0693226   .1067313    -0.65   0.517    -.2801064    .1414613
_Iinterview_11 |  -.2975059   .1021172    -2.91   0.004    -.4991773   -.0958344
_Iinterview_12 |  -.1293688   .1284112    -1.01   0.315    -.3829682    .1242307
_Iinterview_13 |  -.1871876   .0928588    -2.02   0.045    -.3705745   -.0038006
_Iinterview_14 |  -.1373943   .1106024    -1.24   0.216    -.3558231    .0810345
_Iinterview_15 |  -.1036139   .1666984    -0.62   0.535    -.4328269     .225599
_Iinterview_16 |  -.1556906   .1603967    -0.97   0.333    -.4724583    .1610772
_Iinterview_17 |  -.2477957   .1610531    -1.54   0.126    -.5658598    .0702684
_Iinterview_18 |  -.0761009   .1616042    -0.47   0.638    -.3952534    .2430515
_Iinterview_19 |  -.2415893   .1668223    -1.45   0.150     -.571047    .0878684
_Iinterview_20 |  -.1839338   .1714262    -1.07   0.285    -.5224837    .1546161
_Iinterview_21 |  -.0013299   .0688639    -0.02   0.985    -.1373294    .1346696
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |   .0640954   .0524207     1.22   0.223    -.0394304    .1676212
_Iinterview_24 |   .0492176    .048858     1.01   0.315    -.0472721    .1457073
_Iinterview_25 |  -.4220266   .0563788    -7.49   0.000    -.5333691   -.3106841
_Iinterview_26 |  -.2617429   .1211756    -2.16   0.032    -.5010529   -.0224329
_Iinterview_27 |   .0232971   .0634419     0.37   0.714    -.1019945    .1485886
_Iinterview_28 |   .0980821    .186216     0.53   0.599    -.2696762    .4658403
         _cons |   .7412671   .0779544     9.51   0.000     .5873147    .8952194
--------------------------------------------------------------------------------
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_22 omitted because of collinearity

      Source |       SS       df       MS              Number of obs =     860
-------------+------------------------------           F( 57,   802) =    2.04
       Model |  14.3941807    57  .252529486           Prob > F      =  0.0000
    Residual |  99.4139588   802  .123957555           R-squared     =  0.1265
-------------+------------------------------           Adj R-squared =  0.0644
       Total |   113.80814   859  .132489103           Root MSE      =  .35208

--------------------------------------------------------------------------------
       guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |   .0533135   .0355564     1.50   0.134     -.016481     .123108
       hotline |   .0561198   .0351725     1.60   0.111    -.0129212    .1251607
       verdade |   .0033166   .0364443     0.09   0.928    -.0682209    .0748541
           pr1 |  -.0026519   .2602444    -0.01   0.992    -.5134924    .5081886
           pr2 |   .0359442   .3699255     0.10   0.923    -.6901922    .7620806
           pr3 |   -.004915    .410611    -0.01   0.990    -.8109141    .8010841
          post |   .0018885   .0509047     0.04   0.970    -.0980337    .1018106
     post_miss |    .017196   .0845403     0.20   0.839    -.1487504    .1831424
        health |   .0379994   .0303674     1.25   0.211    -.0216095    .0976083
   health_miss |   .1081563   .0989001     1.09   0.274    -.0859772    .3022899
        police |  -.0349627   .0377658    -0.93   0.355    -.1090941    .0391687
   police_miss |  -.2001704   .1546486    -1.29   0.196    -.5037342    .1033934
           sex |    .022706   .0262306     0.87   0.387    -.0287828    .0741948
           age |  -.0008463   .0010202    -0.83   0.407    -.0028489    .0011563
        single |  -.0657188   .0359335    -1.83   0.068    -.1362536    .0048161
         divor |   .0389569   .1374915     0.28   0.777    -.2309289    .3088427
       norelig |  -.0385605   .0638693    -0.60   0.546    -.1639311    .0868102
       protest |   .0051646   .0314505     0.16   0.870    -.0565705    .0668996
           com |   .0214411   .0582777     0.37   0.713    -.0929537     .135836
          prof |   .0514823   .1028666     0.50   0.617    -.1504372    .2534018
       comform |   -.027576   .1241188    -0.22   0.824    -.2712121    .2160601
      econfood |   -.012857   .0115852    -1.11   0.267     -.035598    .0098839
         house |   .0706894   .0417002     1.70   0.090     -.011165    .1525439
          oven |    .046106   .0499389     0.92   0.356    -.0519204    .1441324
        lchang |   .0704687     .06603     1.07   0.286    -.0591434    .2000808
        llomue |   .0572086   .0533931     1.07   0.284    -.0475981    .1620152
       lchuabo |  -.0268976   .0501474    -0.54   0.592    -.1253333     .071538
      lchitewe |  -.0144853   .1402926    -0.10   0.918    -.2898693    .2608988
        lronga |   -.074492   .0550063    -1.35   0.176    -.1824653    .0334813
       chitsua |   .1239226   .1246727     0.99   0.321    -.1208008     .368646
        living |   .0387591   .0124741     3.11   0.002     .0142734    .0632447
 _Iinterview_2 |   .0163333   .1677223     0.10   0.922    -.3128933    .3455598
 _Iinterview_3 |  -.0480822    .408548    -0.12   0.906    -.8500319    .7538674
 _Iinterview_4 |  -.1441309   .4037574    -0.36   0.721    -.9366769    .6484152
 _Iinterview_5 |  -.0194603   .5379628    -0.04   0.971    -1.075442    1.036521
 _Iinterview_6 |  -.2142765   .4120254    -0.52   0.603    -1.023052     .594499
 _Iinterview_7 |  -.1812519   .4053855    -0.45   0.655    -.9769938    .6144901
 _Iinterview_8 |  -.0760195   .4095281    -0.19   0.853    -.8798929    .7278539
 _Iinterview_9 |  -.2413516   .3714298    -0.65   0.516    -.9704409    .4877376
_Iinterview_10 |  -.0693226   .4021147    -0.17   0.863    -.8586441     .719999
_Iinterview_11 |  -.2975059   .3722143    -0.80   0.424    -1.028135    .4331234
_Iinterview_12 |  -.1293688   .3774912    -0.34   0.732    -.8703562    .6116187
_Iinterview_13 |  -.1871876   .3721423    -0.50   0.615    -.9176755    .5433004
_Iinterview_14 |  -.1373943   .3741608    -0.37   0.714    -.8718443    .5970557
_Iinterview_15 |  -.1036139   .2763937    -0.37   0.708    -.6461543    .4389265
_Iinterview_16 |  -.1556906     .27017    -0.58   0.565    -.6860144    .3746332
_Iinterview_17 |  -.2477957   .2675508    -0.93   0.355    -.7729783    .2773869
_Iinterview_18 |  -.0761009   .2670784    -0.28   0.776    -.6003562    .4481544
_Iinterview_19 |  -.2415893   .2675509    -0.90   0.367     -.766772    .2835933
_Iinterview_20 |  -.1839338   .2620869    -0.70   0.483     -.698391    .3305234
_Iinterview_21 |  -.0013299   .0795029    -0.02   0.987    -.1573883    .1547286
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |   .0640954   .0768289     0.83   0.404    -.0867141    .2149049
_Iinterview_24 |   .0492176    .074696     0.66   0.510    -.0974052    .1958404
_Iinterview_25 |  -.4220266   .1874323    -2.25   0.025    -.7899425   -.0541108
_Iinterview_26 |  -.2617429   .1007444    -2.60   0.010    -.4594967   -.0639891
_Iinterview_27 |   .0232971   .0913004     0.26   0.799    -.1559189     .202513
_Iinterview_28 |   .0980821   .4435397     0.22   0.825    -.7725537    .9687179
         _cons |   .7412671   .0833051     8.90   0.000     .5777452    .9047889
--------------------------------------------------------------------------------
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_5 omitted because of collinearity
note: _Iinterview_15 omitted because of collinearity
note: _Iinterview_22 omitted because of collinearity
note: _Iinterview_28 omitted because of collinearity

Linear regression                                      Number of obs =     401
                                                       F( 51,   142) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.1837
                                                       Root MSE      =  .35622

                                     (Std. Err. adjusted for 143 clusters in ea)
--------------------------------------------------------------------------------
               |               Robust
       guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |   .0502838   .0666696     0.75   0.452    -.0815094    .1820769
       hotline |   .0379393    .049801     0.76   0.447     -.060508    .1363865
       verdade |   .1095261   .0474458     2.31   0.022     .0157347    .2033176
           pr1 |  -.0204187   .1076711    -0.19   0.850    -.2332641    .1924266
           pr2 |   .0858527   .1814966     0.47   0.637    -.2729319    .4446372
           pr3 |  -.0080163   .2526621    -0.03   0.975    -.5074816    .4914489
          post |   .0271344   .0556611     0.49   0.627     -.082897    .1371659
     post_miss |  -.0050938    .043532    -0.12   0.907    -.0911484    .0809607
        health |   -.004004   .0397049    -0.10   0.920    -.0824931    .0744852
   health_miss |   .2712407    .066566     4.07   0.000     .1396524     .402829
        police |  -.0069292   .0481946    -0.14   0.886    -.1022008    .0883424
   police_miss |  -.3669493   .2355678    -1.56   0.122    -.8326223    .0987237
           sex |  -.0371905    .036642    -1.01   0.312    -.1096248    .0352439
           age |  -.0021642   .0020766    -1.04   0.299    -.0062692    .0019409
        single |  -.0188657   .0592972    -0.32   0.751    -.1360851    .0983537
         divor |  -.2142224   .2745929    -0.78   0.437    -.7570407     .328596
       norelig |   .0397022   .0956444     0.42   0.679    -.1493687     .228773
       protest |   .0279592   .0569512     0.49   0.624    -.0846224    .1405409
           com |  -.0860279   .0976103    -0.88   0.380     -.278985    .1069292
          prof |  -.0724054   .1102617    -0.66   0.512    -.2903719    .1455612
       comform |  -.4771928   .2063383    -2.31   0.022    -.8850845   -.0693011
      econfood |  -.0051307   .0153821    -0.33   0.739    -.0355382    .0252769
         house |   .0633321   .0689554     0.92   0.360    -.0729798     .199644
          oven |  -.0522848   .0752044    -0.70   0.488    -.2009497    .0963801
        lchang |    -.04584   .1042797    -0.44   0.661    -.2519812    .1603013
        llomue |  -.0229567   .0700102    -0.33   0.743    -.1613536    .1154402
       lchuabo |  -.1232758   .0852182    -1.45   0.150    -.2917361    .0451845
      lchitewe |  -.4852538   .2389876    -2.03   0.044    -.9576871   -.0128205
        lronga |  -.0426989   .0807133    -0.53   0.598    -.2022539     .116856
       chitsua |   .1278627   .1459896     0.88   0.383    -.1607312    .4164565
        living |   .0440302   .0205083     2.15   0.033     .0034893    .0845712
 _Iinterview_2 |   .0842658    .073977     1.14   0.257    -.0619727    .2305044
 _Iinterview_3 |  -.0376503    .223786    -0.17   0.867    -.4800329    .4047322
 _Iinterview_4 |  -.0696727   .2068091    -0.34   0.737    -.4784951    .3391498
 _Iinterview_5 |          0  (omitted)
 _Iinterview_6 |  -.0789428   .2514434    -0.31   0.754    -.5759988    .4181132
 _Iinterview_7 |   .0134717   .1976693     0.07   0.946    -.3772831    .4042265
 _Iinterview_8 |  -.0086218   .2195665    -0.04   0.969    -.4426631    .4254196
 _Iinterview_9 |  -.3265664   .1986517    -1.64   0.102    -.7192632    .0661304
_Iinterview_10 |  -.0440582   .1116818    -0.39   0.694    -.2648321    .1767157
_Iinterview_11 |  -.2657933   .1994588    -1.33   0.185    -.6600857    .1284991
_Iinterview_12 |   .0468982   .1438731     0.33   0.745    -.2375116    .3313081
_Iinterview_13 |   -.265684    .138584    -1.92   0.057    -.5396383    .0082703
_Iinterview_14 |  -.1537885   .1868988    -0.82   0.412    -.5232521    .2156751
_Iinterview_15 |          0  (omitted)
_Iinterview_16 |  -.0169874   .1439445    -0.12   0.906    -.3015384    .2675637
_Iinterview_17 |  -.1365042   .1678619    -0.81   0.417    -.4683355    .1953271
_Iinterview_18 |   .0768476   .1024586     0.75   0.454    -.1256937    .2793889
_Iinterview_19 |  -.0703886   .1398033    -0.50   0.615    -.3467532     .205976
_Iinterview_20 |  -.2246795   .1638995    -1.37   0.173    -.5486779    .0993188
_Iinterview_21 |  -.2106498   .1075728    -1.96   0.052    -.4233009    .0020014
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |   .0280533   .0934658     0.30   0.765     -.156711    .2128176
_Iinterview_24 |  -.0137888   .0650033    -0.21   0.832     -.142288    .1147104
_Iinterview_25 |  -.4203353   .0843121    -4.99   0.000    -.5870043   -.2536662
_Iinterview_26 |  -.1191649    .112742    -1.06   0.292    -.3420346    .1037048
_Iinterview_27 |  -.0325979   .1362059    -0.24   0.811    -.3018513    .2366554
_Iinterview_28 |          0  (omitted)
         _cons |   .8527906   .1140679     7.48   0.000     .6272999    1.078281
--------------------------------------------------------------------------------
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_5 omitted because of collinearity
note: _Iinterview_15 omitted because of collinearity
note: _Iinterview_22 omitted because of collinearity
note: _Iinterview_28 omitted because of collinearity

      Source |       SS       df       MS              Number of obs =     401
-------------+------------------------------           F( 54,   346) =    1.44
       Model |  9.87992684    54  .182961608           Prob > F      =  0.0291
    Residual |  43.9056093   346  .126894825           R-squared     =  0.1837
-------------+------------------------------           Adj R-squared =  0.0563
       Total |  53.7855362   400   .13446384           Root MSE      =  .35622

--------------------------------------------------------------------------------
       guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |   .0502838   .0613157     0.82   0.413    -.0703147    .1708822
       hotline |   .0379393    .057813     0.66   0.512    -.0757699    .1516484
       verdade |   .1095261    .063861     1.72   0.087    -.0160786    .2351308
           pr1 |  -.0204187   .1752188    -0.12   0.907    -.3650468    .3242093
           pr2 |   .0858527   .5283296     0.16   0.871    -.9532891    1.124994
           pr3 |  -.0080163   .6552058    -0.01   0.990    -1.296704    1.280671
          post |   .0271344   .0677341     0.40   0.689     -.106088    .1603569
     post_miss |  -.0050938   .0992391    -0.05   0.959    -.2002816    .1900939
        health |   -.004004   .0492951    -0.08   0.935    -.1009598    .0929519
   health_miss |   .2712407   .1732759     1.57   0.118    -.0695659    .6120473
        police |  -.0069292   .0569327    -0.12   0.903    -.1189069    .1050485
   police_miss |  -.3669493   .2715442    -1.35   0.177    -.9010344    .1671358
           sex |  -.0371905   .0394901    -0.94   0.347    -.1148613    .0404804
           age |  -.0021642   .0016468    -1.31   0.190    -.0054031    .0010748
        single |  -.0188657   .0523226    -0.36   0.719    -.1217762    .0840448
         divor |  -.2142224   .2192884    -0.98   0.329    -.6455284    .2170837
       norelig |   .0397022   .1110277     0.36   0.721     -.178672    .2580763
       protest |   .0279592   .0509049     0.55   0.583    -.0721627    .1280812
           com |  -.0860279   .1038088    -0.83   0.408    -.2902037    .1181479
          prof |  -.0724054   .1390382    -0.52   0.603    -.3458719    .2010612
       comform |  -.4771928   .1558838    -3.06   0.002    -.7837919   -.1705937
      econfood |  -.0051307   .0183593    -0.28   0.780    -.0412406    .0309793
         house |   .0633321   .0640755     0.99   0.324    -.0626943    .1893586
          oven |  -.0522848   .0812189    -0.64   0.520    -.2120296    .1074601
        lchang |    -.04584   .0982319    -0.47   0.641    -.2390468    .1473668
        llomue |  -.0229567   .0881074    -0.26   0.795    -.1962502    .1503368
       lchuabo |  -.1232758   .0821685    -1.50   0.134    -.2848884    .0383368
      lchitewe |  -.4852538   .1756337    -2.76   0.006    -.8306979   -.1398097
        lronga |  -.0426989   .0762137    -0.56   0.576    -.1925993    .1072015
       chitsua |   .1278627   .2228966     0.57   0.567    -.3105402    .5662655
        living |   .0440302   .0185363     2.38   0.018     .0075722    .0804883
 _Iinterview_2 |   .0842658   .2657535     0.32   0.751    -.4384297    .6069614
 _Iinterview_3 |  -.0376503   .6517258    -0.06   0.954    -1.319493    1.244193
 _Iinterview_4 |  -.0696727   .6494152    -0.11   0.915    -1.346971    1.207626
 _Iinterview_5 |          0  (omitted)
 _Iinterview_6 |  -.0789428   .6593605    -0.12   0.905    -1.375802    1.217916
 _Iinterview_7 |   .0134717   .6418494     0.02   0.983    -1.248946    1.275889
 _Iinterview_8 |  -.0086218   .6522076    -0.01   0.989    -1.291412    1.274169
 _Iinterview_9 |  -.3265664   .5276732    -0.62   0.536    -1.364417    .7112843
_Iinterview_10 |  -.0440582   .3708729    -0.12   0.906    -.7735073    .6853909
_Iinterview_11 |  -.2657933   .5315271    -0.50   0.617    -1.311224    .7796376
_Iinterview_12 |   .0468982   .5315991     0.09   0.930    -.9986743    1.092471
_Iinterview_13 |   -.265684   .5238667    -0.51   0.612    -1.296048    .7646801
_Iinterview_14 |  -.1537885   .5271494    -0.29   0.771    -1.190609    .8830321
_Iinterview_15 |          0  (omitted)
_Iinterview_16 |  -.0169874   .1661093    -0.10   0.919    -.3436985    .3097238
_Iinterview_17 |  -.1365042   .1661779    -0.82   0.412    -.4633502    .1903418
_Iinterview_18 |   .0768476   .1611694     0.48   0.634    -.2401475    .3938427
_Iinterview_19 |  -.0703886   .1613406    -0.44   0.663    -.3877205    .2469433
_Iinterview_20 |  -.2246795   .1680675    -1.34   0.182    -.5552421     .105883
_Iinterview_21 |  -.2106498   .1176312    -1.79   0.074    -.4420119    .0207124
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |   .0280533   .1168469     0.24   0.810    -.2017663     .257873
_Iinterview_24 |  -.0137888   .1096281    -0.13   0.900    -.2294102    .2018326
_Iinterview_25 |  -.4203353   .2015842    -2.09   0.038    -.8168199   -.0238506
_Iinterview_26 |  -.1191649   .1694852    -0.70   0.482    -.4525159    .2141861
_Iinterview_27 |  -.0325979   .1457598    -0.22   0.823    -.3192846    .2540888
_Iinterview_28 |          0  (omitted)
         _cons |   .8527906   .1220458     6.99   0.000     .6127455    1.092836
--------------------------------------------------------------------------------

Simultaneous results for guebas2_4_2a, guebas2_4_3a

                                                  Number of obs   =       1014

                                         (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------------
                   |               Robust
                   |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
guebas2_4_2a_mean  |
         civiceduc |   .0533135   .0317519     1.68   0.093    -.0089191    .1155462
           hotline |   .0561198   .0309031     1.82   0.069    -.0044491    .1166887
           verdade |   .0033166    .040598     0.08   0.935     -.076254    .0828872
               pr1 |  -.0026519    .134196    -0.02   0.984    -.2656712    .2603674
               pr2 |   .0359442   .1122093     0.32   0.749     -.183982    .2558703
               pr3 |   -.004915   .1606826    -0.03   0.976    -.3198471     .310017
              post |   .0018885   .0553806     0.03   0.973    -.1066555    .1104325
         post_miss |    .017196   .0529892     0.32   0.746    -.0866609     .121053
            health |   .0379994   .0251593     1.51   0.131     -.011312    .0873107
       health_miss |   .1081563   .0648782     1.67   0.096    -.0190026    .2353152
            police |  -.0349627   .0463146    -0.75   0.450    -.1257376    .0558123
       police_miss |  -.2001704   .1268691    -1.58   0.115    -.4488293    .0484885
               sex |    .022706   .0233582     0.97   0.331    -.0230752    .0684872
               age |  -.0008463   .0010862    -0.78   0.436    -.0029752    .0012825
            single |  -.0657188   .0378921    -1.73   0.083    -.1399859    .0085483
             divor |   .0389569   .1204052     0.32   0.746     -.197033    .2749468
           norelig |  -.0385605    .063717    -0.61   0.545    -.1634434    .0863225
           protest |   .0051646   .0339612     0.15   0.879    -.0613982    .0717273
               com |   .0214411   .0499348     0.43   0.668    -.0764293    .1193115
              prof |   .0514823   .0720319     0.71   0.475    -.0896976    .1926622
           comform |   -.027576   .1179019    -0.23   0.815    -.2586594    .2035074
          econfood |   -.012857   .0115364    -1.11   0.265    -.0354679    .0097539
             house |   .0706894   .0444576     1.59   0.112    -.0164458    .1578246
              oven |    .046106   .0432625     1.07   0.287    -.0386869    .1308989
            lchang |   .0704687   .0780888     0.90   0.367    -.0825825    .2235199
            llomue |   .0572086   .0641628     0.89   0.373    -.0685482    .1829653
           lchuabo |  -.0268976   .0593803    -0.45   0.651    -.1432808    .0894856
          lchitewe |  -.0144853   .1909736    -0.08   0.940    -.3887867    .3598162
            lronga |   -.074492   .0540731    -1.38   0.168    -.1804733    .0314894
           chitsua |   .1239226   .1036148     1.20   0.232    -.0791588    .3270039
            living |   .0387591   .0136186     2.85   0.004      .012067    .0654511
     _Iinterview_2 |   .0163333   .0561208     0.29   0.771    -.0936614     .126328
     _Iinterview_3 |  -.0480822   .1249943    -0.38   0.700    -.2930667    .1969022
     _Iinterview_4 |  -.1441309   .1136507    -1.27   0.205    -.3668822    .0786205
     _Iinterview_5 |  -.0194603   .1158064    -0.17   0.867    -.2464367    .2075161
     _Iinterview_6 |  -.2142765   .1327724    -1.61   0.107    -.4745056    .0459526
     _Iinterview_7 |  -.1812519   .1074718    -1.69   0.092    -.3918926    .0293889
     _Iinterview_8 |  -.0760195   .1255858    -0.61   0.545    -.3221632    .1701242
     _Iinterview_9 |  -.2413516   .1040314    -2.32   0.020    -.4452495   -.0374537
    _Iinterview_10 |  -.0693226   .1031294    -0.67   0.501    -.2714524    .1328073
    _Iinterview_11 |  -.2975059    .098671    -3.02   0.003    -.4908975   -.1041142
    _Iinterview_12 |  -.1293688   .1240776    -1.04   0.297    -.3725565     .113819
    _Iinterview_13 |  -.1871876    .089725    -2.09   0.037    -.3630454   -.0113297
    _Iinterview_14 |  -.1373943   .1068698    -1.29   0.199    -.3468552    .0720667
    _Iinterview_15 |  -.1036139   .1610727    -0.64   0.520    -.4193107    .2120828
    _Iinterview_16 |  -.1556906   .1549837    -1.00   0.315    -.4594531    .1480719
    _Iinterview_17 |  -.2477957    .155618    -1.59   0.111    -.5528013      .05721
    _Iinterview_18 |  -.0761009   .1561505    -0.49   0.626    -.3821502    .2299484
    _Iinterview_19 |  -.2415893   .1611925    -1.50   0.134    -.5575208    .0743421
    _Iinterview_20 |  -.1839338    .165641    -1.11   0.267    -.5085842    .1407166
    _Iinterview_21 |  -.0013299     .06654    -0.02   0.984    -.1317458     .129086
    _Iinterview_22 |          0  (omitted)
    _Iinterview_23 |   .0640954   .0506517     1.27   0.206    -.0351801    .1633708
    _Iinterview_24 |   .0492176   .0472091     1.04   0.297    -.0433106    .1417458
    _Iinterview_25 |  -.4220266   .0544761    -7.75   0.000    -.5287979   -.3152554
    _Iinterview_26 |  -.2617429   .1170863    -2.24   0.025    -.4912278    -.032258
    _Iinterview_27 |   .0232971   .0613009     0.38   0.704    -.0968505    .1434447
    _Iinterview_28 |   .0980821   .1799317     0.55   0.586    -.2545775    .4507417
             _cons |   .7412671   .0753237     9.84   0.000     .5936354    .8888987
-------------------+----------------------------------------------------------------
guebas2_4_2a_lnvar |
             _cons |  -2.087816   .0624769   -33.42   0.000    -2.210269   -1.965364
-------------------+----------------------------------------------------------------
guebas2_4_3a_mean  |
         civiceduc |   .0502838   .0619819     0.81   0.417    -.0711986    .1717661
           hotline |   .0379393   .0462994     0.82   0.413     -.052806    .1286845
           verdade |   .1095261   .0441098     2.48   0.013     .0230725    .1959798
               pr1 |  -.0204187   .1001005    -0.20   0.838    -.2166121    .1757746
               pr2 |   .0858527   .1687352     0.51   0.611    -.2448623    .4165676
               pr3 |  -.0080163   .2348969    -0.03   0.973    -.4684058    .4523731
              post |   .0271344   .0517474     0.52   0.600    -.0742887    .1285575
         post_miss |  -.0050938   .0404712    -0.13   0.900    -.0844159    .0742282
            health |   -.004004   .0369132    -0.11   0.914    -.0763525    .0683445
       health_miss |   .2712407   .0618856     4.38   0.000     .1499472    .3925342
            police |  -.0069292   .0448059    -0.15   0.877    -.0947472    .0808888
       police_miss |  -.3669493   .2190045    -1.68   0.094    -.7961902    .0622917
               sex |  -.0371905   .0340656    -1.09   0.275    -.1039579     .029577
               age |  -.0021642   .0019306    -1.12   0.262     -.005948    .0016197
            single |  -.0188657   .0551279    -0.34   0.732    -.1269144     .089183
             divor |  -.2142224   .2552857    -0.84   0.401    -.7145732    .2861285
           norelig |   .0397022   .0889194     0.45   0.655    -.1345767     .213981
           protest |   .0279592   .0529468     0.53   0.597    -.0758146    .1317331
               com |  -.0860279   .0907471    -0.95   0.343     -.263889    .0918332
              prof |  -.0724054    .102509    -0.71   0.480    -.2733193    .1285085
           comform |  -.4771928   .1918302    -2.49   0.013     -.853173   -.1012126
          econfood |  -.0051307   .0143006    -0.36   0.720    -.0331592    .0228979
             house |   .0633321    .064107     0.99   0.323    -.0623154    .1889796
              oven |  -.0522848   .0699166    -0.75   0.455    -.1893188    .0847493
            lchang |    -.04584   .0969476    -0.47   0.636    -.2358537    .1441737
            llomue |  -.0229567   .0650876    -0.35   0.724    -.1505261    .1046127
           lchuabo |  -.1232758   .0792263    -1.56   0.120    -.2785566     .032005
          lchitewe |  -.4852538   .2221839    -2.18   0.029    -.9207261   -.0497814
            lronga |  -.0426989   .0750382    -0.57   0.569     -.189771    .1043732
           chitsua |   .1278627   .1357248     0.94   0.346     -.138153    .3938783
            living |   .0440302   .0190663     2.31   0.021      .006661    .0813994
     _Iinterview_2 |   .0842658   .0687755     1.23   0.220    -.0505317    .2190634
     _Iinterview_3 |  -.0376503   .2080511    -0.18   0.856     -.445423    .3701223
     _Iinterview_4 |  -.0696727   .1922679    -0.36   0.717    -.4465108    .3071654
     _Iinterview_5 |          0  (omitted)
     _Iinterview_6 |  -.0789428   .2337638    -0.34   0.736    -.5371115    .3792259
     _Iinterview_7 |   .0134717   .1837707     0.07   0.942    -.3467123    .3736557
     _Iinterview_8 |  -.0086218   .2041283    -0.04   0.966    -.4087058    .3914623
     _Iinterview_9 |  -.3265664    .184684    -1.77   0.077    -.6885405    .0354077
    _Iinterview_10 |  -.0440582   .1038293    -0.42   0.671    -.2475598    .1594434
    _Iinterview_11 |  -.2657933   .1854344    -1.43   0.152    -.6292382    .0976515
    _Iinterview_12 |   .0468982    .133757     0.35   0.726    -.2152608    .3090572
    _Iinterview_13 |   -.265684   .1288398    -2.06   0.039    -.5182054   -.0131626
    _Iinterview_14 |  -.1537885   .1737576    -0.89   0.376     -.494347    .1867701
    _Iinterview_15 |          0  (omitted)
    _Iinterview_16 |  -.0169874   .1338234    -0.13   0.899    -.2792765    .2453018
    _Iinterview_17 |  -.1365042   .1560592    -0.87   0.382    -.4423746    .1693662
    _Iinterview_18 |   .0768476   .0952545     0.81   0.420    -.1098479     .263543
    _Iinterview_19 |  -.0703886   .1299734    -0.54   0.588    -.3251318    .1843546
    _Iinterview_20 |  -.2246795   .1523754    -1.47   0.140    -.5233298    .0739708
    _Iinterview_21 |  -.2106498   .1000091    -2.11   0.035    -.4066641   -.0146355
    _Iinterview_22 |          0  (omitted)
    _Iinterview_23 |   .0280533    .086894     0.32   0.747    -.1422559    .1983625
    _Iinterview_24 |  -.0137888   .0604328    -0.23   0.820    -.1322348    .1046572
    _Iinterview_25 |  -.4203353   .0783839    -5.36   0.000    -.5739649   -.2667056
    _Iinterview_26 |  -.1191649   .1048149    -1.14   0.256    -.3245984    .0862685
    _Iinterview_27 |  -.0325979    .126629    -0.26   0.797    -.2807862    .2155903
    _Iinterview_28 |          0  (omitted)
             _cons |   .8527906   .1060475     8.04   0.000     .6449412     1.06064
-------------------+----------------------------------------------------------------
guebas2_4_3a_lnvar |
             _cons |  -2.064397   .0847854   -24.35   0.000    -2.230573    -1.89822
------------------------------------------------------------------------------------

 ( 1)  [guebas2_4_2a_mean]civiceduc - [guebas2_4_3a_mean]civiceduc = 0

           chi2(  1) =    0.00
         Prob > chi2 =    0.9586
.95856985

 ( 1)  [guebas2_4_2a_mean]hotline - [guebas2_4_3a_mean]hotline = 0

           chi2(  1) =    0.16
         Prob > chi2 =    0.6916
.6916123

 ( 1)  [guebas2_4_2a_mean]verdade - [guebas2_4_3a_mean]verdade = 0

           chi2(  1) =    3.99
         Prob > chi2 =    0.0458
.04581585

. 
. matrix define means=(m_guebas2_2_1, m_guebas2_3_1, m_guebas2_4_1 \ t_guebas2_2_1_1, t_guebas2_
> 3_1_1, t_guebas2_4_1_1 \ t_guebas2_2_1_2, t_guebas2_3_1_2, t_guebas2_4_1_2 \ t_guebas2_2_1_3, 
> t_guebas2_3_1_3, t_guebas2_4_1_3 \ t_guebas2_2_1_4, t_guebas2_3_1_4, t_guebas2_4_1_4 \ t_gueba
> s2_2_5, t_guebas2_3_5, t_guebas2_4_5 \ t_guebas2_2_6, t_guebas2_3_6, t_guebas2_4_6 \ t_guebas2
> _2_7, t_guebas2_3_7, t_guebas2_4_7)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_voting.xml") replace sheet("votin
> g 1") 


note: results saved to outputregs_voting.xml

. xml_tab $list2, save("outputregs_voting.xml") append sheet("voting 1 stats") 


note: results saved to outputregs_voting.xml

. estimates clear

. 
. global list1=""

. global list2=""

. 
. foreach i in $voting2 {
  2. 
.         regress `i' $treat $prov if time==1, cluster(ea)
  3.         estimates store `i'_2_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2_1=r(mean)
  6.         display m_`i'_2_1
  7.         test civiceduc = hotline
  8.         scalar define t_`i'_2_1_1=r(p)
  9.         display t_`i'_2_1_1
 10.         test civiceduc = verdade
 11.         scalar define t_`i'_2_1_2=r(p)
 12.         display t_`i'_2_1_2
 13.         test hotline = verdade
 14.         scalar define t_`i'_2_1_3=r(p)
 15.         display t_`i'_2_1_3
 16.         test civiceduc hotline verdade
 17.         scalar define t_`i'_2_1_4=r(p)
 18.         display t_`i'_2_1_4
 19. 
.         regress `i' $treat $prov if time==1 & lazy==0, cluster(ea)
 20.         estimates store `i'_2_2
 21.         regress `i' $treat $prov if time==1 & lazy==0
 22.         estimates store `i'_2_2a
 23.         
.         regress `i' $treat $prov if time==1 & (lazy==1|control==1), cluster(ea)
 24.         estimates store `i'_2_3
 25.         regress `i' $treat $prov if time==1 & (lazy==1|control==1)
 26.         estimates store `i'_2_3a
 27. 
.         suest `i'_2_2a `i'_2_3a, cluster(ea)
 28.         test [`i'_2_2a_mean]civiceduc=[`i'_2_3a_mean]civiceduc  
 29.         scalar define t_`i'_2_5=r(p)
 30.         display t_`i'_2_5
 31.         test [`i'_2_2a_mean]hotline=[`i'_2_3a_mean]hotline      
 32.         scalar define t_`i'_2_6=r(p)
 33.         display t_`i'_2_6
 34.         test [`i'_2_2a_mean]verdade=[`i'_2_3a_mean]verdade
 35.         scalar define t_`i'_2_7=r(p)
 36.         display t_`i'_2_7
 37. 
.         regress `i' $treat $prov $ea $controls if time==1, cluster(ea)
 38.         estimates store `i'_3_1
 39.         sum `i' if e(sample) & control == 1
 40.         scalar define m_`i'_3_1=r(mean)
 41.         display m_`i'_3_1
 42.         test civiceduc = hotline
 43.         scalar define t_`i'_3_1_1=r(p)
 44.         display t_`i'_3_1_1
 45.         test civiceduc = verdade
 46.         scalar define t_`i'_3_1_2=r(p)
 47.         display t_`i'_3_1_2
 48.         test hotline = verdade
 49.         scalar define t_`i'_3_1_3=r(p)
 50.         display t_`i'_3_1_3
 51.         test civiceduc hotline verdade
 52.         scalar define t_`i'_3_1_4=r(p)
 53.         display t_`i'_3_1_4
 54. 
.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0, cluster(ea)
 55.         estimates store `i'_3_2
 56.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0
 57.         estimates store `i'_3_2a
 58.         
.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1), cluster(ea)
 59.         estimates store `i'_3_3
 60.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1)
 61.         estimates store `i'_3_3a
 62. 
.         suest `i'_3_2a `i'_3_3a, cluster(ea)
 63.         test [`i'_3_2a_mean]civiceduc=[`i'_3_3a_mean]civiceduc  
 64.         scalar define t_`i'_3_5=r(p)
 65.         display t_`i'_3_5
 66.         test [`i'_3_2a_mean]hotline=[`i'_3_3a_mean]hotline      
 67.         scalar define t_`i'_3_6=r(p)
 68.         display t_`i'_3_6
 69.         test [`i'_3_2a_mean]verdade=[`i'_3_3a_mean]verdade
 70.         scalar define t_`i'_3_7=r(p)
 71.         display t_`i'_3_7
 72. 
.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1, cluster(ea)
 73.         estimates store `i'_4_1
 74.         sum `i' if e(sample) & control == 1
 75.         scalar define m_`i'_4_1=r(mean)
 76.         display m_`i'_4_1
 77.         test civiceduc = hotline
 78.         scalar define t_`i'_4_1_1=r(p)
 79.         display t_`i'_4_1_1
 80.         test civiceduc = verdade
 81.         scalar define t_`i'_4_1_2=r(p)
 82.         display t_`i'_4_1_2
 83.         test hotline = verdade
 84.         scalar define t_`i'_4_1_3=r(p)
 85.         display t_`i'_4_1_3
 86.         test civiceduc hotline verdade
 87.         scalar define t_`i'_4_1_4=r(p)
 88.         display t_`i'_4_1_4
 89. 
.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & lazy==0, cluster
> (ea)
 90.         estimates store `i'_4_2
 91.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & lazy==0
 92.         estimates store `i'_4_2a
 93.         
.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & (lazy==1|control
> ==1), cluster(ea)
 94.         estimates store `i'_4_3
 95.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & (lazy==1|cont
> rol==1)
 96.         estimates store `i'_4_3a
 97. 
.         suest `i'_4_2a `i'_4_3a, cluster(ea)
 98.         test [`i'_4_2a_mean]civiceduc=[`i'_4_3a_mean]civiceduc  
 99.         scalar define t_`i'_4_5=r(p)
100.         display t_`i'_4_5
101.         test [`i'_4_2a_mean]hotline=[`i'_4_3a_mean]hotline      
102.         scalar define t_`i'_4_6=r(p)
103.         display t_`i'_4_6
104.         test [`i'_4_2a_mean]verdade=[`i'_4_3a_mean]verdade
105.         scalar define t_`i'_4_7=r(p)
106.         display t_`i'_4_7
107.         
.         global list1="$list1" + " `i'_2_1" + " `i'_3_1" + " `i'_4_1" + " `i'_2_2" + " `i'_3_2"
>  + " `i'_4_2"  + " `i'_2_3" + " `i'_3_3" + " `i'_4_3"
108.         
.         }

Linear regression                                      Number of obs =    1031
                                                       F(  6,   160) =    1.41
                                                       Prob > F      =  0.2154
                                                       R-squared     =  0.0081
                                                       Root MSE      =  .11566

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0080255   .0076847    -1.04   0.298     -.023202     .007151
     hotline |   .0076988   .0106212     0.72   0.470    -.0132771    .0286747
     verdade |   .0073859   .0118812     0.62   0.535    -.0160782    .0308501
         pr1 |   .0148059   .0119319     1.24   0.216    -.0087584    .0383703
         pr2 |   .0017487   .0093769     0.19   0.852    -.0167697    .0202671
         pr3 |  -.0068936    .007199    -0.96   0.340    -.0211109    .0073237
       _cons |   .0094358   .0093086     1.01   0.312    -.0089478    .0278194
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
   dlakhama2 |       249    .0120482    .1093208          0          1
.01204819

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    3.27
            Prob > F =    0.0724
.07242772

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    2.31
            Prob > F =    0.1302
.13022268

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.00
            Prob > F =    0.9799
.9799386

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.77
            Prob > F =    0.1551
.15506698

Linear regression                                      Number of obs =     872
                                                       F(  6,   160) =    1.20
                                                       Prob > F      =  0.3104
                                                       R-squared     =  0.0101
                                                       Root MSE      =  .12106

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.007328   .0080577    -0.91   0.364    -.0232411    .0085851
     hotline |   .0080357   .0116377     0.69   0.491    -.0149475     .031019
     verdade |   .0121979   .0139348     0.88   0.383    -.0153219    .0397177
         pr1 |   .0207049   .0132818     1.56   0.121    -.0055254    .0469352
         pr2 |   .0060923    .010182     0.60   0.550    -.0140161    .0262006
         pr3 |  -.0036924   .0076405    -0.48   0.630    -.0187818    .0113969
       _cons |   .0060314   .0096614     0.62   0.533    -.0130488    .0251117
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     872
-------------+------------------------------           F(  6,   865) =    1.48
       Model |  .129924508     6  .021654085           Prob > F      =  0.1827
    Residual |  12.6762682   865  .014654645           R-squared     =  0.0101
-------------+------------------------------           Adj R-squared =  0.0033
       Total |  12.8061927   871  .014702862           Root MSE      =  .12106

------------------------------------------------------------------------------
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.007328   .0112016    -0.65   0.513    -.0293135    .0146574
     hotline |   .0080357    .011423     0.70   0.482    -.0143844    .0304559
     verdade |   .0121979    .011539     1.06   0.291    -.0104497    .0348455
         pr1 |   .0207049   .0113402     1.83   0.068    -.0015527    .0429624
         pr2 |   .0060923    .011803     0.52   0.606    -.0170737    .0292582
         pr3 |  -.0036924   .0115902    -0.32   0.750    -.0264406    .0190557
       _cons |   .0060314   .0104577     0.58   0.564     -.014494    .0265568
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     408
                                                       F(  6,   144) =    0.70
                                                       Prob > F      =  0.6519
                                                       R-squared     =  0.0088
                                                       Root MSE      =  .09895

                                   (Std. Err. adjusted for 145 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0117376   .0066482    -1.77   0.080    -.0248783     .001403
     hotline |   .0058943   .0179455     0.33   0.743    -.0295764    .0413649
     verdade |  -.0128001   .0071111    -1.80   0.074    -.0268557    .0012556
         pr1 |  -.0092813   .0157432    -0.59   0.556    -.0403988    .0218363
         pr2 |  -.0073583   .0162682    -0.45   0.652    -.0395136    .0247971
         pr3 |  -.0191942   .0129714    -1.48   0.141    -.0448331    .0064446
       _cons |   .0211634   .0127048     1.67   0.098    -.0039486    .0462754
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     408
-------------+------------------------------           F(  6,   401) =    0.59
       Model |  .034692842     6   .00578214           Prob > F      =  0.7379
    Residual |  3.92609147   401  .009790752           R-squared     =  0.0088
-------------+------------------------------           Adj R-squared = -0.0061
       Total |  3.96078431   407  .009731657           Root MSE      =  .09895

------------------------------------------------------------------------------
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0117376   .0151077    -0.78   0.438    -.0414378    .0179626
     hotline |   .0058943   .0145864     0.40   0.686     -.022781    .0345696
     verdade |  -.0128001   .0153479    -0.83   0.405    -.0429724    .0173723
         pr1 |  -.0092813   .0137508    -0.67   0.500    -.0363139    .0177514
         pr2 |  -.0073583   .0142115    -0.52   0.605    -.0352965      .02058
         pr3 |  -.0191942   .0134773    -1.42   0.155    -.0456892    .0073007
       _cons |   .0211634   .0105197     2.01   0.045     .0004827    .0418442
------------------------------------------------------------------------------

Simultaneous results for dlakhama2_2_2a, dlakhama2_2_3a

                                                  Number of obs   =       1031

                                           (Std. Err. adjusted for 161 clusters in ea)
--------------------------------------------------------------------------------------
                     |               Robust
                     |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
dlakhama2_2_2a_mean  |
           civiceduc |   -.007328   .0080299    -0.91   0.361    -.0230663    .0084103
             hotline |   .0080357   .0115975     0.69   0.488     -.014695    .0307665
             verdade |   .0121979   .0138867     0.88   0.380    -.0150195    .0394153
                 pr1 |   .0207049    .013236     1.56   0.118    -.0052372    .0466469
                 pr2 |   .0060923   .0101468     0.60   0.548    -.0137952    .0259797
                 pr3 |  -.0036924   .0076142    -0.48   0.628     -.018616    .0112311
               _cons |   .0060314    .009628     0.63   0.531    -.0128391     .024902
---------------------+----------------------------------------------------------------
dlakhama2_2_2a_lnvar |
               _cons |  -4.222998   .2758548   -15.31   0.000    -4.763663   -3.682332
---------------------+----------------------------------------------------------------
dlakhama2_2_3a_mean  |
           civiceduc |  -.0117376   .0065967    -1.78   0.075     -.024667    .0011917
             hotline |   .0058943   .0178066     0.33   0.741     -.029006    .0407946
             verdade |  -.0128001   .0070561    -1.81   0.070    -.0266297    .0010296
                 pr1 |  -.0092813   .0156213    -0.59   0.552    -.0398985     .021336
                 pr2 |  -.0073583   .0161423    -0.46   0.649    -.0389966      .02428
                 pr3 |  -.0191942    .012871    -1.49   0.136    -.0444209    .0060324
               _cons |   .0211634   .0126065     1.68   0.093    -.0035448    .0458716
---------------------+----------------------------------------------------------------
dlakhama2_2_3a_lnvar |
               _cons |  -4.626317     .47388    -9.76   0.000    -5.555105   -3.697529
--------------------------------------------------------------------------------------

 ( 1)  [dlakhama2_2_2a_mean]civiceduc - [dlakhama2_2_3a_mean]civiceduc = 0

           chi2(  1) =    0.93
         Prob > chi2 =    0.3352
.33523256

 ( 1)  [dlakhama2_2_2a_mean]hotline - [dlakhama2_2_3a_mean]hotline = 0

           chi2(  1) =    0.01
         Prob > chi2 =    0.9115
.9114717

 ( 1)  [dlakhama2_2_2a_mean]verdade - [dlakhama2_2_3a_mean]verdade = 0

           chi2(  1) =    4.19
         Prob > chi2 =    0.0408
.04075865

Linear regression                                      Number of obs =    1017
                                                       F( 31,   160) =    0.63
                                                       Prob > F      =  0.9348
                                                       R-squared     =  0.0494
                                                       Root MSE      =  .11129

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0151457   .0090036    -1.68   0.094    -.0329269    .0026356
     hotline |  -.0018385   .0102796    -0.18   0.858    -.0221396    .0184627
     verdade |   .0004109   .0117984     0.03   0.972    -.0228897    .0237115
         pr1 |   .0051533   .0192896     0.27   0.790    -.0329417    .0432482
         pr2 |   .0067592   .0100037     0.68   0.500    -.0129971    .0265155
         pr3 |  -.0077262   .0115677    -0.67   0.505    -.0305712    .0151188
        post |  -.0178947   .0124147    -1.44   0.151    -.0424126    .0066231
   post_miss |  -.0094503   .0081542    -1.16   0.248     -.025554    .0066535
      health |  -.0030533   .0073179    -0.42   0.677    -.0175054    .0113987
 health_miss |  -.0173907   .0106581    -1.63   0.105    -.0384394     .003658
      police |   .0017155   .0102328     0.17   0.867    -.0184933    .0219243
 police_miss |    .054275   .0343339     1.58   0.116    -.0135311    .1220811
         sex |    .013505   .0075294     1.79   0.075    -.0013648    .0283748
         age |  -.0001448   .0002848    -0.51   0.612    -.0007073    .0004177
      single |  -.0034026   .0095264    -0.36   0.721    -.0222163     .015411
       divor |   .1015398    .099082     1.02   0.307    -.0941373     .297217
     norelig |   .0177037   .0240997     0.73   0.464    -.0298908    .0652983
     protest |   .0137624   .0090313     1.52   0.130    -.0040736    .0315984
         com |    .000623   .0235629     0.03   0.979    -.0459114    .0471574
        prof |  -.0163218    .009273    -1.76   0.080     -.034635    .0019915
     comform |  -.0086568   .0073894    -1.17   0.243    -.0232501    .0059364
    econfood |   .0035192   .0035838     0.98   0.328    -.0035584    .0105968
       house |  -.0207883   .0153165    -1.36   0.177    -.0510368    .0094603
        oven |  -.0070774   .0058893    -1.20   0.231    -.0187082    .0045533
      lchang |   .0022587   .0098165     0.23   0.818     -.017128    .0216454
      llomue |  -.0201192   .0179731    -1.12   0.265    -.0556144    .0153759
     lchuabo |   .0418006    .025418     1.64   0.102    -.0083974    .0919987
    lchitewe |  -.0346543   .0170682    -2.03   0.044    -.0683622   -.0009463
      lronga |  -.0153759    .008729    -1.76   0.080    -.0326147    .0018629
     chitsua |  -.0191096   .0091251    -2.09   0.038    -.0371307   -.0010884
      living |   -.003458   .0042627    -0.81   0.418    -.0118765    .0049605
       _cons |   .0378206   .0218096     1.73   0.085    -.0052512    .0808923
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
   dlakhama2 |       247    .0121457    .1097588          0          1
.01214575

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    3.05
            Prob > F =    0.0826
.08259073

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    2.67
            Prob > F =    0.1041
.10414066

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.04
            Prob > F =    0.8383
.83833237

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    2.01
            Prob > F =    0.1141
.11409429

Linear regression                                      Number of obs =     862
                                                       F( 31,   160) =    0.63
                                                       Prob > F      =  0.9343
                                                       R-squared     =  0.0573
                                                       Root MSE      =  .12059

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0169738   .0095357    -1.78   0.077    -.0358058    .0018583
     hotline |   .0015369   .0120083     0.13   0.898    -.0221782    .0252521
     verdade |    .003859   .0134968     0.29   0.775    -.0227959    .0305139
         pr1 |   .0044366   .0227863     0.19   0.846    -.0405641    .0494373
         pr2 |    .005303   .0104941     0.51   0.614    -.0154218    .0260278
         pr3 |  -.0113524   .0134325    -0.85   0.399    -.0378803    .0151755
        post |  -.0224487   .0145686    -1.54   0.125    -.0512202    .0063228
   post_miss |   -.008763    .009077    -0.97   0.336    -.0266892    .0091632
      health |  -.0035004   .0087185    -0.40   0.689    -.0207185    .0137177
 health_miss |  -.0183222    .012163    -1.51   0.134    -.0423429    .0056986
      police |   .0030237   .0118176     0.26   0.798     -.020315    .0263624
 police_miss |   .0565584   .0395208     1.43   0.154    -.0214912    .1346081
         sex |    .015546   .0088357     1.76   0.080    -.0019036    .0329956
         age |  -.0002529    .000338    -0.75   0.455    -.0009204    .0004146
      single |  -.0038315   .0116887    -0.33   0.743    -.0269155    .0192524
       divor |   .1311034   .1244691     1.05   0.294    -.1147108    .3769176
     norelig |   .0191447   .0280124     0.68   0.495    -.0361771    .0744666
     protest |   .0128919   .0099862     1.29   0.199    -.0068299    .0326138
         com |   .0017111   .0259814     0.07   0.948    -.0495996    .0530218
        prof |  -.0169017   .0110759    -1.53   0.129    -.0387755    .0049722
     comform |  -.0059281   .0095265    -0.62   0.535     -.024742    .0128858
    econfood |   .0047047   .0041797     1.13   0.262    -.0035498    .0129592
       house |  -.0258963   .0185797    -1.39   0.165    -.0625893    .0107968
        oven |  -.0078534   .0072435    -1.08   0.280    -.0221587    .0064519
      lchang |   .0072649   .0121169     0.60   0.550    -.0166648    .0311947
      llomue |  -.0213275   .0208525    -1.02   0.308    -.0625092    .0198542
     lchuabo |   .0473125   .0286429     1.65   0.101    -.0092544    .1038795
    lchitewe |  -.0439123   .0224794    -1.95   0.053    -.0883068    .0004822
      lronga |  -.0193135   .0099705    -1.94   0.054    -.0390043    .0003773
     chitsua |  -.0214125   .0116545    -1.84   0.068     -.044429     .001604
      living |  -.0028752    .004861    -0.59   0.555    -.0124751    .0067248
       _cons |   .0426588   .0252393     1.69   0.093    -.0071863    .0925038
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     862
-------------+------------------------------           F( 31,   830) =    1.63
       Model |   .73327705    31  .023654098           Prob > F      =  0.0176
    Residual |  12.0706673   830  .014542973           R-squared     =  0.0573
-------------+------------------------------           Adj R-squared =  0.0221
       Total |  12.8039443   861  .014871015           Root MSE      =  .12059

------------------------------------------------------------------------------
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0169738    .011941    -1.42   0.156    -.0404118    .0064643
     hotline |   .0015369   .0118449     0.13   0.897    -.0217126    .0247865
     verdade |    .003859   .0122869     0.31   0.754    -.0202581    .0279762
         pr1 |   .0044366   .0182606     0.24   0.808    -.0314059    .0402791
         pr2 |    .005303   .0247761     0.21   0.831    -.0433282    .0539342
         pr3 |  -.0113524   .0255161    -0.44   0.656    -.0614361    .0387313
        post |  -.0224487   .0170399    -1.32   0.188     -.055895    .0109976
   post_miss |   -.008763   .0285845    -0.31   0.759    -.0648695    .0473435
      health |  -.0035004   .0102363    -0.34   0.732    -.0235924    .0165917
 health_miss |  -.0183222   .0333001    -0.55   0.582    -.0836844    .0470401
      police |   .0030237   .0126381     0.24   0.811    -.0217828    .0278302
 police_miss |   .0565584    .052361     1.08   0.280     -.046217    .1593339
         sex |    .015546   .0088606     1.75   0.080    -.0018458    .0329379
         age |  -.0002529    .000343    -0.74   0.461    -.0009262    .0004204
      single |  -.0038315   .0116087    -0.33   0.741    -.0266174    .0189544
       divor |   .1311034   .0466849     2.81   0.005     .0394689    .2227378
     norelig |   .0191447   .0216729     0.88   0.377    -.0233954    .0616849
     protest |   .0128919    .010526     1.22   0.221    -.0077688    .0335526
         com |   .0017111   .0195016     0.09   0.930    -.0365671    .0399893
        prof |  -.0169017   .0345518    -0.49   0.625    -.0847209    .0509176
     comform |  -.0059281   .0413978    -0.14   0.886    -.0871849    .0753286
    econfood |   .0047047   .0037004     1.27   0.204    -.0025585    .0119679
       house |  -.0258963   .0127501    -2.03   0.043    -.0509224   -.0008701
        oven |  -.0078534   .0164498    -0.48   0.633    -.0401415    .0244346
      lchang |   .0072649   .0219856     0.33   0.741    -.0358889    .0504188
      llomue |  -.0213275    .017836    -1.20   0.232    -.0563364    .0136814
     lchuabo |   .0473125   .0168914     2.80   0.005     .0141577    .0804674
    lchitewe |  -.0439123   .0472368    -0.93   0.353    -.1366299    .0488053
      lronga |  -.0193135   .0177386    -1.09   0.277    -.0541313    .0155043
     chitsua |  -.0214125   .0421217    -0.51   0.611    -.1040901    .0612651
      living |  -.0028752   .0041512    -0.69   0.489    -.0110233    .0052729
       _cons |   .0426588   .0224857     1.90   0.058    -.0014767    .0867943
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     402
                                                       F( 31,   142) =    0.21
                                                       Prob > F      =  1.0000
                                                       R-squared     =  0.0588
                                                       Root MSE      =  .08703

                                   (Std. Err. adjusted for 143 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0190145   .0117398    -1.62   0.108    -.0422219    .0041928
     hotline |  -.0128791   .0077602    -1.66   0.099    -.0282195    .0024613
     verdade |  -.0209089   .0134771    -1.55   0.123    -.0475506    .0057328
         pr1 |  -.0274195   .0314661    -0.87   0.385    -.0896221    .0347831
         pr2 |   .0061584   .0149864     0.41   0.682    -.0234669    .0357836
         pr3 |  -.0063065   .0140069    -0.45   0.653    -.0339954    .0213824
        post |   -.027189   .0215764    -1.26   0.210    -.0698415    .0154635
   post_miss |  -.0113872   .0084343    -1.35   0.179    -.0280601    .0052857
      health |  -.0077837    .014335    -0.54   0.588    -.0361213    .0205538
 health_miss |   -.020178   .0177655    -1.14   0.258    -.0552971    .0149411
      police |   .0166242   .0197151     0.84   0.401    -.0223488    .0555972
 police_miss |  -.0008831   .0145356    -0.06   0.952    -.0296172     .027851
         sex |   .0058896   .0091301     0.65   0.520    -.0121589     .023938
         age |  -.0000745   .0002239    -0.33   0.740    -.0005172    .0003681
      single |  -.0110509   .0070276    -1.57   0.118    -.0249432    .0028413
       divor |  -.0141705   .0129203    -1.10   0.275    -.0397114    .0113705
     norelig |  -.0125241   .0130641    -0.96   0.339    -.0383493    .0133011
     protest |  -.0087536   .0097044    -0.90   0.369    -.0279374    .0104302
         com |  -.0074809   .0063402    -1.18   0.240    -.0200142    .0050524
        prof |   .0040014   .0138276     0.29   0.773    -.0233331    .0313359
     comform |  -.0007543   .0082231    -0.09   0.927    -.0170098    .0155013
    econfood |   .0001939   .0038403     0.05   0.960    -.0073976    .0077854
       house |  -.0101049   .0121097    -0.83   0.405    -.0340436    .0138337
        oven |  -.0038214   .0048641    -0.79   0.433    -.0134367     .005794
      lchang |    .010684   .0120623     0.89   0.377    -.0131608    .0345289
      llomue |    .000425   .0266087     0.02   0.987    -.0521755    .0530254
     lchuabo |   .0595125   .0468555     1.27   0.206    -.0331119    .1521369
    lchitewe |   .0134737   .0238441     0.57   0.573    -.0336615    .0606089
      lronga |  -.0113457   .0128153    -0.89   0.377    -.0366792    .0139877
     chitsua |  -.0114076   .0184795    -0.62   0.538     -.047938    .0251228
      living |  -.0039343   .0048229    -0.82   0.416    -.0134682    .0055997
       _cons |    .043772   .0304122     1.44   0.152    -.0163473    .1038912
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     402
-------------+------------------------------           F( 31,   370) =    0.75
       Model |  .174948071    31  .005643486           Prob > F      =  0.8391
    Residual |  2.80266387   370  .007574767           R-squared     =  0.0588
-------------+------------------------------           Adj R-squared = -0.0201
       Total |  2.97761194   401  .007425466           Root MSE      =  .08703

------------------------------------------------------------------------------
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0190145   .0144941    -1.31   0.190    -.0475157    .0094866
     hotline |  -.0128791   .0136268    -0.95   0.345    -.0396748    .0139165
     verdade |  -.0209089   .0147485    -1.42   0.157    -.0499102    .0080924
         pr1 |  -.0274195   .0211333    -1.30   0.195     -.068976     .014137
         pr2 |   .0061584   .0265781     0.23   0.817    -.0461047    .0584214
         pr3 |  -.0063065   .0272112    -0.23   0.817    -.0598146    .0472015
        post |   -.027189   .0161881    -1.68   0.094    -.0590212    .0046432
   post_miss |  -.0113872   .0236001    -0.48   0.630    -.0577944    .0350199
      health |  -.0077837   .0115187    -0.68   0.500    -.0304341    .0148667
 health_miss |   -.020178   .0417637    -0.48   0.629    -.1023019    .0619459
      police |   .0166242   .0135491     1.23   0.221    -.0100187     .043267
 police_miss |  -.0008831    .065558    -0.01   0.989    -.1297961      .12803
         sex |   .0058896   .0094454     0.62   0.533    -.0126838    .0244629
         age |  -.0000745   .0003907    -0.19   0.849    -.0008428    .0006937
      single |  -.0110509   .0118035    -0.94   0.350    -.0342614    .0121595
       divor |  -.0141705   .0515111    -0.28   0.783    -.1154617    .0871208
     norelig |  -.0125241   .0268332    -0.47   0.641    -.0652887    .0402405
     protest |  -.0087536   .0121188    -0.72   0.471     -.032584    .0150768
         com |  -.0074809   .0246922    -0.30   0.762    -.0560355    .0410737
        prof |   .0040014   .0327423     0.12   0.903    -.0603829    .0683857
     comform |  -.0007543    .036662    -0.02   0.984    -.0728464    .0713378
    econfood |   .0001939    .004147     0.05   0.963    -.0079607    .0083485
       house |  -.0101049   .0134357    -0.75   0.452    -.0365248    .0163149
        oven |  -.0038214   .0182303    -0.21   0.834    -.0396693    .0320266
      lchang |    .010684   .0233911     0.46   0.648    -.0353122    .0566803
      llomue |    .000425    .020839     0.02   0.984    -.0405527    .0414026
     lchuabo |   .0595125   .0196932     3.02   0.003      .020788    .0982371
    lchitewe |   .0134737   .0421999     0.32   0.750    -.0695079    .0964553
      lronga |  -.0113457   .0181294    -0.63   0.532    -.0469954    .0243039
     chitsua |  -.0114076   .0531064    -0.21   0.830    -.1158359    .0930207
      living |  -.0039343    .004375    -0.90   0.369    -.0125373    .0046688
       _cons |    .043772   .0231888     1.89   0.060    -.0018264    .0893703
------------------------------------------------------------------------------

Simultaneous results for dlakhama2_3_2a, dlakhama2_3_3a

                                                  Number of obs   =       1017

                                           (Std. Err. adjusted for 161 clusters in ea)
--------------------------------------------------------------------------------------
                     |               Robust
                     |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
dlakhama2_3_2a_mean  |
           civiceduc |  -.0169738   .0093625    -1.81   0.070    -.0353238    .0013763
             hotline |   .0015369   .0117901     0.13   0.896    -.0215712    .0246451
             verdade |    .003859   .0132516     0.29   0.771    -.0221137    .0298317
                 pr1 |   .0044366   .0223723     0.20   0.843    -.0394123    .0482856
                 pr2 |    .005303   .0103034     0.51   0.607    -.0148914    .0254974
                 pr3 |  -.0113524   .0131885    -0.86   0.389    -.0372014    .0144966
                post |  -.0224487   .0143039    -1.57   0.117    -.0504839    .0055865
           post_miss |   -.008763   .0089121    -0.98   0.325    -.0262304    .0087044
              health |  -.0035004   .0085601    -0.41   0.683    -.0202778    .0132771
         health_miss |  -.0183222    .011942    -1.53   0.125    -.0417282    .0050838
              police |   .0030237   .0116029     0.26   0.794    -.0197177     .025765
         police_miss |   .0565584   .0388028     1.46   0.145    -.0194937    .1326105
                 sex |    .015546   .0086752     1.79   0.073     -.001457     .032549
                 age |  -.0002529   .0003319    -0.76   0.446    -.0009034    .0003975
              single |  -.0038315   .0114763    -0.33   0.738    -.0263247    .0186616
               divor |   .1311034   .1222078     1.07   0.283    -.1084196    .3706263
             norelig |   .0191447   .0275035     0.70   0.486    -.0347612    .0730507
             protest |   .0128919   .0098048     1.31   0.189    -.0063252     .032109
                 com |   .0017111   .0255094     0.07   0.947    -.0482864    .0517085
                prof |  -.0169017   .0108747    -1.55   0.120    -.0382157    .0044123
             comform |  -.0059281   .0093534    -0.63   0.526    -.0242605    .0124043
            econfood |   .0047047   .0041038     1.15   0.252    -.0033386     .012748
               house |  -.0258963   .0182421    -1.42   0.156    -.0616502    .0098577
                oven |  -.0078534    .007112    -1.10   0.269    -.0217926    .0060857
              lchang |   .0072649   .0118968     0.61   0.541    -.0160523    .0305822
              llomue |  -.0213275   .0204737    -1.04   0.298    -.0614552    .0188002
             lchuabo |   .0473125   .0281226     1.68   0.092    -.0078067    .1024318
            lchitewe |  -.0439123    .022071    -1.99   0.047    -.0871706    -.000654
              lronga |  -.0193135   .0097894    -1.97   0.049    -.0385003   -.0001266
             chitsua |  -.0214125   .0114428    -1.87   0.061    -.0438399     .001015
              living |  -.0028752   .0047727    -0.60   0.547    -.0122294    .0064791
               _cons |   .0426588   .0247807     1.72   0.085    -.0059105    .0912281
---------------------+----------------------------------------------------------------
dlakhama2_3_2a_lnvar |
               _cons |  -4.230647   .2581455   -16.39   0.000    -4.736603   -3.724692
---------------------+----------------------------------------------------------------
dlakhama2_3_3a_mean  |
           civiceduc |  -.0190145   .0112724    -1.69   0.092    -.0411081     .003079
             hotline |  -.0128791   .0074513    -1.73   0.084    -.0274833    .0017251
             verdade |  -.0209089   .0129406    -1.62   0.106     -.046272    .0044542
                 pr1 |  -.0274195   .0302135    -0.91   0.364    -.0866368    .0317978
                 pr2 |   .0061584   .0143898     0.43   0.669    -.0220451    .0343618
                 pr3 |  -.0063065   .0134493    -0.47   0.639    -.0326666    .0200536
                post |   -.027189   .0207175    -1.31   0.189    -.0677945    .0134165
           post_miss |  -.0113872   .0080985    -1.41   0.160      -.02726    .0044856
              health |  -.0077837   .0137643    -0.57   0.572    -.0347613    .0191939
         health_miss |   -.020178   .0170583    -1.18   0.237    -.0536117    .0132556
              police |   .0166242   .0189303     0.88   0.380    -.0204785    .0537268
         police_miss |  -.0008831   .0139569    -0.06   0.950    -.0282382     .026472
                 sex |   .0058896   .0087666     0.67   0.502    -.0112927    .0230718
                 age |  -.0000745    .000215    -0.35   0.729     -.000496    .0003469
              single |  -.0110509   .0067478    -1.64   0.101    -.0242765    .0021746
               divor |  -.0141705   .0124059    -1.14   0.253    -.0384857    .0101447
             norelig |  -.0125241    .012544    -1.00   0.318    -.0371099    .0120617
             protest |  -.0087536   .0093181    -0.94   0.348    -.0270168    .0095095
                 com |  -.0074809   .0060878    -1.23   0.219    -.0194127    .0044509
                prof |   .0040014   .0132771     0.30   0.763    -.0220213    .0300241
             comform |  -.0007543   .0078958    -0.10   0.924    -.0162297    .0147211
            econfood |   .0001939   .0036874     0.05   0.958    -.0070332     .007421
               house |  -.0101049   .0116277    -0.87   0.385    -.0328947    .0126849
                oven |  -.0038214   .0046704    -0.82   0.413    -.0129753    .0053326
              lchang |    .010684   .0115821     0.92   0.356    -.0120165    .0333846
              llomue |    .000425   .0255495     0.02   0.987    -.0496511     .050501
             lchuabo |   .0595125   .0449902     1.32   0.186    -.0286667    .1476917
            lchitewe |   .0134737   .0228949     0.59   0.556    -.0313994    .0583468
              lronga |  -.0113457   .0123052    -0.92   0.357    -.0354634    .0127719
             chitsua |  -.0114076   .0177438    -0.64   0.520    -.0461848    .0233697
              living |  -.0039343   .0046309    -0.85   0.396    -.0130107    .0051421
               _cons |    .043772   .0292016     1.50   0.134    -.0134621     .101006
---------------------+----------------------------------------------------------------
dlakhama2_3_3a_lnvar |
               _cons |  -4.882933   .4916614    -9.93   0.000    -5.846571   -3.919294
--------------------------------------------------------------------------------------

 ( 1)  [dlakhama2_3_2a_mean]civiceduc - [dlakhama2_3_3a_mean]civiceduc = 0

           chi2(  1) =    0.09
         Prob > chi2 =    0.7682
.76817445

 ( 1)  [dlakhama2_3_2a_mean]hotline - [dlakhama2_3_3a_mean]hotline = 0

           chi2(  1) =    2.41
         Prob > chi2 =    0.1207
.12066259

 ( 1)  [dlakhama2_3_2a_mean]verdade - [dlakhama2_3_3a_mean]verdade = 0

           chi2(  1) =    3.97
         Prob > chi2 =    0.0463
.04631358
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_22 omitted because of collinearity

Linear regression                                      Number of obs =    1014
                                                       F( 54,   160) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.0702
                                                       Root MSE      =  .11172

                                     (Std. Err. adjusted for 161 clusters in ea)
--------------------------------------------------------------------------------
               |               Robust
     dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |  -.0170446   .0095407    -1.79   0.076    -.0358866    .0017973
       hotline |  -.0033141   .0103365    -0.32   0.749    -.0237277    .0170996
       verdade |   .0002307   .0115977     0.02   0.984    -.0226736    .0231349
           pr1 |   -.007542   .0407698    -0.18   0.853    -.0880584    .0729744
           pr2 |  -.0162524   .0111294    -1.46   0.146    -.0382319    .0057272
           pr3 |  -.0187421   .0213913    -0.88   0.382    -.0609879    .0235037
          post |  -.0179426   .0124177    -1.44   0.150    -.0424664    .0065811
     post_miss |  -.0082286   .0083006    -0.99   0.323    -.0246214    .0081642
        health |  -.0006172   .0074367    -0.08   0.934    -.0153039    .0140695
   health_miss |  -.0149911   .0114315    -1.31   0.192    -.0375673    .0075851
        police |   .0014699   .0100619     0.15   0.884    -.0184014    .0213412
   police_miss |   .0533841    .035886     1.49   0.139    -.0174871    .1242553
           sex |   .0128866   .0073854     1.74   0.083    -.0016988    .0274719
           age |  -.0001767   .0002827    -0.63   0.533     -.000735    .0003816
        single |  -.0017554   .0105706    -0.17   0.868    -.0226314    .0191206
         divor |    .106973   .0996935     1.07   0.285    -.0899118    .3038578
       norelig |   .0184101   .0233011     0.79   0.431    -.0276072    .0644275
       protest |   .0145058   .0096432     1.50   0.134    -.0045386    .0335502
           com |  -.0020824   .0244718    -0.09   0.932    -.0504119     .046247
          prof |   -.014473   .0124124    -1.17   0.245    -.0389862    .0100403
       comform |  -.0193513   .0160323    -1.21   0.229    -.0510135     .012311
      econfood |   .0028732   .0038482     0.75   0.456    -.0047267    .0104731
         house |  -.0173719    .015994    -1.09   0.279    -.0489584    .0142147
          oven |  -.0106637   .0066574    -1.60   0.111    -.0238114     .002484
        lchang |   .0011706   .0079672     0.15   0.883     -.014564    .0169051
        llomue |  -.0163837   .0169437    -0.97   0.335    -.0498458    .0170785
       lchuabo |   .0421457   .0257059     1.64   0.103    -.0086209    .0929123
      lchitewe |   -.032967   .0181555    -1.82   0.071    -.0688224    .0028884
        lronga |  -.0219146   .0126226    -1.74   0.084    -.0468431    .0030139
       chitsua |  -.0244928   .0129795    -1.89   0.061     -.050126    .0011405
        living |   -.003966   .0041818    -0.95   0.344    -.0122246    .0042926
 _Iinterview_2 |   .0045677   .0086402     0.53   0.598    -.0124959    .0216313
 _Iinterview_3 |   .0125566   .0216316     0.58   0.562    -.0301637     .055277
 _Iinterview_4 |   .0131116   .0179343     0.73   0.466    -.0223069    .0485301
 _Iinterview_5 |   .0053677   .0193526     0.28   0.782    -.0328518    .0435873
 _Iinterview_6 |   .0037713   .0226865     0.17   0.868    -.0410323    .0485749
 _Iinterview_7 |   .0319535   .0178099     1.79   0.075    -.0032194    .0671264
 _Iinterview_8 |     .01733   .0201228     0.86   0.390    -.0224105    .0570705
 _Iinterview_9 |   .0181689   .0095757     1.90   0.060    -.0007422      .03708
_Iinterview_10 |   .0286291   .0125592     2.28   0.024      .003826    .0534322
_Iinterview_11 |   .0427377   .0240226     1.78   0.077    -.0047046    .0901801
_Iinterview_12 |   .0441749   .0343624     1.29   0.200    -.0236875    .1120373
_Iinterview_13 |   .0151082   .0104466     1.45   0.150    -.0055228    .0357392
_Iinterview_14 |   .0515773     .03054     1.69   0.093    -.0087361    .1118908
_Iinterview_15 |  -.0068401   .0345604    -0.20   0.843    -.0750934    .0614132
_Iinterview_16 |  -.0072796   .0335658    -0.22   0.829    -.0735689    .0590096
_Iinterview_17 |   .0455464   .0410839     1.11   0.269    -.0355903     .126683
_Iinterview_18 |  -.0120997   .0342208    -0.35   0.724    -.0796824     .055483
_Iinterview_19 |   .0259707   .0438743     0.59   0.555    -.0606767    .1126181
_Iinterview_20 |   .0403301   .0365886     1.10   0.272    -.0319287    .1125889
_Iinterview_21 |  -.0023576   .0071284    -0.33   0.741    -.0164354    .0117203
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |   .0174277   .0238799     0.73   0.467    -.0297327    .0645882
_Iinterview_24 |  -.0047432   .0042669    -1.11   0.268    -.0131699    .0036834
_Iinterview_25 |   -.001002   .0141737    -0.07   0.944    -.0289935    .0269896
_Iinterview_26 |    .044397   .0482864     0.92   0.359    -.0509638    .1397578
_Iinterview_27 |  -.0028144   .0070637    -0.40   0.691    -.0167646    .0111357
_Iinterview_28 |   -.035347   .0389537    -0.91   0.366    -.1122768    .0415828
         _cons |    .032642    .024691     1.32   0.188    -.0161202    .0814042
--------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
   dlakhama2 |       247    .0121457    .1097588          0          1
.01214575

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    2.90
            Prob > F =    0.0906
.09055105

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    2.99
            Prob > F =    0.0856
.08555599

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.10
            Prob > F =    0.7553
.75531017

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.94
            Prob > F =    0.1247
.12473609
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_22 omitted because of collinearity

Linear regression                                      Number of obs =     860
                                                       F( 53,   160) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.0815
                                                       Root MSE      =  .12109

                                     (Std. Err. adjusted for 161 clusters in ea)
--------------------------------------------------------------------------------
               |               Robust
     dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |  -.0186829    .010409    -1.79   0.075    -.0392396    .0018738
       hotline |  -.0003197   .0119637    -0.03   0.979    -.0239469    .0233074
       verdade |   .0031332   .0134759     0.23   0.816    -.0234803    .0297468
           pr1 |  -.0084806   .0482791    -0.18   0.861    -.1038271    .0868658
           pr2 |  -.0149826   .0216707    -0.69   0.490    -.0577802     .027815
           pr3 |  -.0237478   .0294137    -0.81   0.421    -.0818369    .0343413
          post |   -.021579    .014434    -1.50   0.137    -.0500848    .0069268
     post_miss |  -.0081683   .0097696    -0.84   0.404    -.0274624    .0111257
        health |  -.0009457   .0090287    -0.10   0.917    -.0187764     .016885
   health_miss |  -.0147392   .0140285    -1.05   0.295     -.042444    .0129656
        police |   .0023575    .011336     0.21   0.836      -.02003     .024745
   police_miss |   .0560692   .0421728     1.33   0.186     -.027218    .1393563
           sex |   .0147663   .0086601     1.71   0.090    -.0023365    .0318691
           age |  -.0002813    .000343    -0.82   0.413    -.0009588    .0003961
        single |  -.0023743   .0128505    -0.18   0.854    -.0277527    .0230041
         divor |   .1455196    .124197     1.17   0.243    -.0997573    .3907965
       norelig |    .020331   .0265658     0.77   0.445    -.0321338    .0727957
       protest |   .0135589   .0102827     1.32   0.189    -.0067484    .0338662
           com |  -.0017433   .0271487    -0.06   0.949    -.0553594    .0518728
          prof |  -.0177395   .0160085    -1.11   0.269    -.0493548    .0138758
       comform |  -.0198834   .0210001    -0.95   0.345    -.0613565    .0215898
      econfood |   .0036825   .0045069     0.82   0.415    -.0052181    .0125831
         house |   -.017995   .0190238    -0.95   0.346    -.0555651    .0195752
          oven |  -.0111296   .0080569    -1.38   0.169    -.0270412     .004782
        lchang |   .0027869   .0094926     0.29   0.769      -.01596    .0215338
        llomue |  -.0155867   .0195813    -0.80   0.427    -.0542578    .0230843
       lchuabo |   .0478807   .0289914     1.65   0.101    -.0093745     .105136
      lchitewe |   -.050482   .0248851    -2.03   0.044    -.0996276   -.0013365
        lronga |  -.0274124   .0145706    -1.88   0.062    -.0561879     .001363
       chitsua |   -.026809   .0158749    -1.69   0.093    -.0581604    .0045424
        living |  -.0034982   .0048651    -0.72   0.473    -.0131062    .0061098
 _Iinterview_2 |   .0108234   .0114576     0.94   0.346    -.0118042     .033451
 _Iinterview_3 |   .0191238   .0293575     0.65   0.516    -.0388543     .077102
 _Iinterview_4 |   .0178839   .0254884     0.70   0.484    -.0324531    .0682209
 _Iinterview_5 |   .0098757   .0280364     0.35   0.725    -.0454935    .0652449
 _Iinterview_6 |   .0136368   .0302702     0.45   0.653    -.0461438    .0734174
 _Iinterview_7 |   .0412123   .0260012     1.59   0.115    -.0101375    .0925621
 _Iinterview_8 |    .025301   .0294767     0.86   0.392    -.0329125    .0835145
 _Iinterview_9 |   .0191937   .0193808     0.99   0.324    -.0190816    .0574689
_Iinterview_10 |   .0314721    .026413     1.19   0.235     -.020691    .0836352
_Iinterview_11 |   .0446468   .0330393     1.35   0.178    -.0206026    .1098961
_Iinterview_12 |   .0567754   .0492225     1.15   0.250    -.0404342    .1539851
_Iinterview_13 |   .0146223   .0216551     0.68   0.501    -.0281443    .0573889
_Iinterview_14 |   .0588493   .0403369     1.46   0.147    -.0208121    .1385107
_Iinterview_15 |  -.0048227   .0421247    -0.11   0.909    -.0880148    .0783693
_Iinterview_16 |  -.0104651   .0410206    -0.26   0.799    -.0914768    .0705466
_Iinterview_17 |   .0511911   .0489032     1.05   0.297    -.0453878    .1477701
_Iinterview_18 |  -.0131297   .0414239    -0.32   0.752    -.0949378    .0686785
_Iinterview_19 |   .0330984   .0517311     0.64   0.523    -.0690653    .1352622
_Iinterview_20 |    .048853   .0442212     1.10   0.271    -.0384796    .1361856
_Iinterview_21 |   .0005703   .0081147     0.07   0.944    -.0154553     .016596
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |   .0215552   .0276764     0.78   0.437    -.0331029    .0762133
_Iinterview_24 |  -.0021069   .0060897    -0.35   0.730    -.0141334    .0099197
_Iinterview_25 |    .002125   .0162786     0.13   0.896    -.0300237    .0342737
_Iinterview_26 |   .0565903   .0554761     1.02   0.309    -.0529695    .1661502
_Iinterview_27 |  -.0030707   .0088887    -0.35   0.730     -.020625    .0144836
_Iinterview_28 |  -.0388518   .0474232    -0.82   0.414     -.132508    .0548043
         _cons |   .0312154    .028254     1.10   0.271    -.0245834    .0870142
--------------------------------------------------------------------------------
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_22 omitted because of collinearity

      Source |       SS       df       MS              Number of obs =     860
-------------+------------------------------           F( 57,   802) =    1.25
       Model |   1.0440158    57  .018316067           Prob > F      =  0.1075
    Residual |  11.7594726   802  .014662684           R-squared     =  0.0815
-------------+------------------------------           Adj R-squared =  0.0163
       Total |  12.8034884   859  .014905109           Root MSE      =  .12109

--------------------------------------------------------------------------------
     dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |  -.0186829   .0122289    -1.53   0.127    -.0426874    .0053215
       hotline |  -.0003197   .0120969    -0.03   0.979     -.024065    .0234255
       verdade |   .0031332   .0125343     0.25   0.803    -.0214707    .0277371
           pr1 |  -.0084806   .0895059    -0.09   0.925    -.1841741    .1672128
           pr2 |  -.0149826   .1272285    -0.12   0.906    -.2647228    .2347576
           pr3 |  -.0237478   .1412215    -0.17   0.867    -.3009551    .2534596
          post |   -.021579   .0175077    -1.23   0.218    -.0559453    .0127872
     post_miss |  -.0081683    .029076    -0.28   0.779    -.0652423    .0489056
        health |  -.0009457   .0104443    -0.09   0.928     -.021447    .0195556
   health_miss |  -.0147392   .0340147    -0.43   0.665    -.0815076    .0520292
        police |   .0023575   .0129888     0.18   0.856    -.0231385    .0278535
   police_miss |   .0560692   .0531883     1.05   0.292    -.0483356    .1604739
           sex |   .0147663   .0090215     1.64   0.102    -.0029422    .0324749
           age |  -.0002813   .0003509    -0.80   0.423    -.0009701    .0004074
        single |  -.0023743   .0123586    -0.19   0.848    -.0266334    .0218848
         divor |   .1455196   .0472875     3.08   0.002     .0526978    .2383415
       norelig |    .020331   .0219666     0.93   0.355    -.0227878    .0634497
       protest |   .0135589   .0108168     1.25   0.210    -.0076737    .0347914
           com |  -.0017433   .0200435    -0.09   0.931    -.0410871    .0376006
          prof |  -.0177395   .0353789    -0.50   0.616    -.0871857    .0517067
       comform |  -.0198834   .0426882    -0.47   0.641    -.1036772    .0639104
      econfood |   .0036825   .0039845     0.92   0.356    -.0041388    .0115038
         house |   -.017995    .014342    -1.25   0.210    -.0461472    .0101572
          oven |  -.0111296   .0171755    -0.65   0.517    -.0448438    .0225846
        lchang |   .0027869   .0227097     0.12   0.902    -.0417906    .0473644
        llomue |  -.0155867   .0183635    -0.85   0.396    -.0516329    .0204594
       lchuabo |   .0478807   .0172472     2.78   0.006     .0140258    .0817357
      lchitewe |   -.050482   .0482509    -1.05   0.296    -.1451949    .0442308
        lronga |  -.0274124   .0189183    -1.45   0.148    -.0645477    .0097228
       chitsua |   -.026809   .0428787    -0.63   0.532    -.1109767    .0573588
        living |  -.0034982   .0042902    -0.82   0.415    -.0119196    .0049231
 _Iinterview_2 |   .0108234   .0576848     0.19   0.851    -.1024076    .1240543
 _Iinterview_3 |   .0191238    .140512     0.14   0.892    -.2566908    .2949385
 _Iinterview_4 |   .0178839   .1388643     0.13   0.898    -.2546966    .2904644
 _Iinterview_5 |   .0098757   .1850216     0.05   0.957    -.3533081    .3730594
 _Iinterview_6 |   .0136368    .141708     0.10   0.923    -.2645255    .2917991
 _Iinterview_7 |   .0412123   .1394243     0.30   0.768    -.2324674    .3148919
 _Iinterview_8 |    .025301    .140849     0.18   0.857    -.2511753    .3017773
 _Iinterview_9 |   .0191937   .1277459     0.15   0.881    -.2315621    .2699494
_Iinterview_10 |   .0314721   .1382994     0.23   0.820    -.2399994    .3029436
_Iinterview_11 |   .0446468   .1280157     0.35   0.727    -.2066387    .2959322
_Iinterview_12 |   .0567754   .1298306     0.44   0.662    -.1980725    .3116233
_Iinterview_13 |   .0146223    .127991     0.11   0.909    -.2366145    .2658591
_Iinterview_14 |   .0588493   .1286852     0.46   0.648    -.1937502    .3114488
_Iinterview_15 |  -.0048227   .0950601    -0.05   0.960    -.1914187    .1817733
_Iinterview_16 |  -.0104651   .0929196    -0.11   0.910    -.1928595    .1719292
_Iinterview_17 |   .0511911   .0920188     0.56   0.578     -.129435    .2318173
_Iinterview_18 |  -.0131297   .0918563    -0.14   0.886    -.1934369    .1671775
_Iinterview_19 |   .0330984   .0920188     0.36   0.719    -.1475277    .2137246
_Iinterview_20 |    .048853   .0901396     0.54   0.588    -.1280843    .2257904
_Iinterview_21 |   .0005703   .0273435     0.02   0.983    -.0531029    .0542435
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |   .0215552   .0264238     0.82   0.415    -.0303127    .0734231
_Iinterview_24 |  -.0021069   .0256902    -0.08   0.935    -.0525349    .0483212
_Iinterview_25 |    .002125   .0644636     0.03   0.974    -.1244124    .1286623
_Iinterview_26 |   .0565903    .034649     1.63   0.103    -.0114232    .1246038
_Iinterview_27 |  -.0030707    .031401    -0.10   0.922    -.0647085    .0585671
_Iinterview_28 |  -.0388518   .1525467    -0.25   0.799    -.3382897     .260586
         _cons |   .0312154   .0286512     1.09   0.276    -.0250247    .0874555
--------------------------------------------------------------------------------
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_5 omitted because of collinearity
note: _Iinterview_15 omitted because of collinearity
note: _Iinterview_22 omitted because of collinearity
note: _Iinterview_28 omitted because of collinearity

Linear regression                                      Number of obs =     401
                                                       F( 51,   142) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.1010
                                                       Root MSE      =  .08796

                                     (Std. Err. adjusted for 143 clusters in ea)
--------------------------------------------------------------------------------
               |               Robust
     dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |  -.0205165   .0126211    -1.63   0.106    -.0454661    .0044331
       hotline |  -.0150281   .0092771    -1.62   0.107    -.0333672     .003311
       verdade |  -.0155716   .0121997    -1.28   0.204     -.039688    .0085449
           pr1 |  -.0209999   .0321957    -0.65   0.515    -.0846447    .0426449
           pr2 |   .0148146   .0153245     0.97   0.335     -.015479    .0451083
           pr3 |   .0214149   .0427334     0.50   0.617    -.0630609    .1058907
          post |  -.0269647   .0203325    -1.33   0.187    -.0671582    .0132288
     post_miss |   -.011391   .0080453    -1.42   0.159    -.0272949     .004513
        health |  -.0092785   .0167527    -0.55   0.581    -.0423954    .0238383
   health_miss |  -.0251081   .0258113    -0.97   0.332    -.0761322     .025916
        police |   .0174996   .0180099     0.97   0.333    -.0181025    .0531018
   police_miss |   -.009845   .0305368    -0.32   0.748    -.0702104    .0505205
           sex |   .0052809   .0085349     0.62   0.537     -.011591    .0221528
           age |  -.0000253   .0002457    -0.10   0.918     -.000511    .0004605
        single |  -.0122675   .0096746    -1.27   0.207    -.0313924    .0068574
         divor |  -.0067381    .009581    -0.70   0.483    -.0256778    .0122017
       norelig |  -.0229314   .0202892    -1.13   0.260    -.0630393    .0171765
       protest |  -.0107939   .0115841    -0.93   0.353    -.0336935    .0121057
           com |  -.0051602   .0084798    -0.61   0.544    -.0219231    .0116027
          prof |   .0166444   .0154537     1.08   0.283    -.0139046    .0471934
       comform |  -.0035051   .0173912    -0.20   0.841    -.0378842     .030874
      econfood |   .0018746   .0048327     0.39   0.699    -.0076788     .011428
         house |  -.0080601   .0171917    -0.47   0.640    -.0420449    .0259246
          oven |   -.009847   .0102701    -0.96   0.339    -.0301491    .0104551
        lchang |   .0038386   .0090835     0.42   0.673    -.0141177    .0217949
        llomue |  -.0021782   .0277519    -0.08   0.938    -.0570385    .0526821
       lchuabo |   .0528474   .0432494     1.22   0.224    -.0326484    .1383432
      lchitewe |   .0110068   .0349039     0.32   0.753    -.0579916    .0800052
        lronga |  -.0152503   .0180859    -0.84   0.401    -.0510027    .0205021
       chitsua |  -.0022572   .0140939    -0.16   0.873    -.0301181    .0256038
        living |  -.0044786   .0045731    -0.98   0.329    -.0135187    .0045616
 _Iinterview_2 |  -.0066041   .0135132    -0.49   0.626    -.0333171    .0201089
 _Iinterview_3 |  -.0101813   .0394754    -0.26   0.797    -.0882168    .0678541
 _Iinterview_4 |  -.0068826   .0324747    -0.21   0.832     -.071079    .0573137
 _Iinterview_5 |          0  (omitted)
 _Iinterview_6 |  -.0191907   .0509375    -0.38   0.707    -.1198845    .0815031
 _Iinterview_7 |  -.0163124   .0361578    -0.45   0.653    -.0877895    .0551647
 _Iinterview_8 |  -.0112513   .0369077    -0.30   0.761    -.0842109    .0617083
 _Iinterview_9 |  -.0054448   .0143303    -0.38   0.705    -.0337732    .0228836
_Iinterview_10 |   .0132922   .0180351     0.74   0.462    -.0223597    .0489441
_Iinterview_11 |  -.0033814   .0141751    -0.24   0.812    -.0314029      .02464
_Iinterview_12 |  -.0042545   .0135451    -0.31   0.754    -.0310305    .0225215
_Iinterview_13 |   .0071281   .0097441     0.73   0.466    -.0121341    .0263904
_Iinterview_14 |   .0582245   .0546008     1.07   0.288    -.0497109      .16616
_Iinterview_15 |          0  (omitted)
_Iinterview_16 |   -.008508   .0145097    -0.59   0.559     -.037191     .020175
_Iinterview_17 |    .001428    .014149     0.10   0.920     -.026542    .0293979
_Iinterview_18 |   -.008638    .015186    -0.57   0.570    -.0386578    .0213817
_Iinterview_19 |   .0354488   .0430484     0.82   0.412    -.0496498    .1205474
_Iinterview_20 |   .0025802   .0151548     0.17   0.865     -.027378    .0325384
_Iinterview_21 |   .0003008   .0085428     0.04   0.972    -.0165866    .0171883
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |    .059941   .0626231     0.96   0.340    -.0638529     .183735
_Iinterview_24 |  -.0055371   .0054095    -1.02   0.308    -.0162307    .0051564
_Iinterview_25 |  -.0081177   .0162177    -0.50   0.617    -.0401769    .0239416
_Iinterview_26 |   .0026821   .0105217     0.25   0.799    -.0181174    .0234816
_Iinterview_27 |  -.0103152   .0111128    -0.93   0.355    -.0322831    .0116528
_Iinterview_28 |          0  (omitted)
         _cons |   .0343479   .0349608     0.98   0.328    -.0347631    .1034588
--------------------------------------------------------------------------------
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_5 omitted because of collinearity
note: _Iinterview_15 omitted because of collinearity
note: _Iinterview_22 omitted because of collinearity
note: _Iinterview_28 omitted because of collinearity

      Source |       SS       df       MS              Number of obs =     401
-------------+------------------------------           F( 54,   346) =    0.72
       Model |  .300668552    54  .005567936           Prob > F      =  0.9297
    Residual |  2.67688756   346  .007736669           R-squared     =  0.1010
-------------+------------------------------           Adj R-squared = -0.0393
       Total |  2.97755611   400   .00744389           Root MSE      =  .08796

--------------------------------------------------------------------------------
     dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |  -.0205165     .01514    -1.36   0.176    -.0502946    .0092616
       hotline |  -.0150281   .0142752    -1.05   0.293    -.0431051    .0130489
       verdade |  -.0155716   .0157685    -0.99   0.324    -.0465858    .0154426
           pr1 |  -.0209999   .0432649    -0.49   0.628    -.1060952    .0640954
           pr2 |   .0148146   .1304548     0.11   0.910    -.2417696    .2713988
           pr3 |   .0214149    .161783     0.13   0.895    -.2967869    .3396167
          post |  -.0269647   .0167249    -1.61   0.108    -.0598599    .0059305
     post_miss |   -.011391    .024504    -0.46   0.642    -.0595866    .0368047
        health |  -.0092785   .0121719    -0.76   0.446    -.0332188    .0146617
   health_miss |  -.0251081   .0427852    -0.59   0.558    -.1092598    .0590437
        police |   .0174996   .0140578     1.24   0.214    -.0101498    .0451491
   police_miss |   -.009845   .0670495    -0.15   0.883    -.1417209    .1220309
           sex |   .0052809   .0097509     0.54   0.588    -.0138975    .0244594
           age |  -.0000253   .0004066    -0.06   0.951     -.000825    .0007745
        single |  -.0122675   .0129195    -0.95   0.343     -.037678    .0131431
         divor |  -.0067381   .0541465    -0.12   0.901    -.1132358    .0997597
       norelig |  -.0229314   .0274149    -0.84   0.403    -.0768522    .0309894
       protest |  -.0107939   .0125694    -0.86   0.391    -.0355159    .0139282
           com |  -.0051602   .0256324    -0.20   0.841    -.0555752    .0452547
          prof |   .0166444   .0343312     0.48   0.628    -.0508798    .0841686
       comform |  -.0035051   .0384907    -0.09   0.927    -.0792104    .0722001
      econfood |   .0018746   .0045333     0.41   0.679    -.0070416    .0107909
         house |  -.0080601   .0158215    -0.51   0.611    -.0391785    .0230582
          oven |   -.009847   .0200545    -0.49   0.624    -.0492911    .0295971
        lchang |   .0038386   .0242554     0.16   0.874    -.0438679    .0515451
        llomue |  -.0021782   .0217554    -0.10   0.920    -.0449677    .0406113
       lchuabo |   .0528474    .020289     2.60   0.010     .0129422    .0927527
      lchitewe |   .0110068   .0433674     0.25   0.800      -.07429    .0963036
        lronga |  -.0152503   .0188186    -0.81   0.418    -.0522636     .021763
       chitsua |  -.0022572   .0550375    -0.04   0.967    -.1105073     .105993
        living |  -.0044786    .004577    -0.98   0.329    -.0134808    .0045236
 _Iinterview_2 |  -.0066041   .0656197    -0.10   0.920    -.1356677    .1224595
 _Iinterview_3 |  -.0101813   .1609237    -0.06   0.950    -.3266931    .3063304
 _Iinterview_4 |  -.0068826   .1603531    -0.04   0.966    -.3222722     .308507
 _Iinterview_5 |          0  (omitted)
 _Iinterview_6 |  -.0191907   .1628088    -0.12   0.906    -.3394103    .3010289
 _Iinterview_7 |  -.0163124    .158485    -0.10   0.918    -.3280277    .2954029
 _Iinterview_8 |  -.0112513   .1610427    -0.07   0.944     -.327997    .3054945
 _Iinterview_9 |  -.0054448   .1302927    -0.04   0.967    -.2617102    .2508206
_Iinterview_10 |   .0132922   .0915757     0.15   0.885    -.1668229    .1934072
_Iinterview_11 |  -.0033814   .1312443    -0.03   0.979    -.2615185    .2547556
_Iinterview_12 |  -.0042545   .1312621    -0.03   0.974    -.2624266    .2539175
_Iinterview_13 |   .0071281   .1293528     0.06   0.956    -.2472887    .2615449
_Iinterview_14 |   .0582245   .1301634     0.45   0.655    -.1977865    .3142356
_Iinterview_15 |          0  (omitted)
_Iinterview_16 |   -.008508   .0410156    -0.21   0.836    -.0891793    .0721633
_Iinterview_17 |    .001428   .0410325     0.03   0.972    -.0792766    .0821325
_Iinterview_18 |   -.008638   .0397958    -0.22   0.828    -.0869102    .0696342
_Iinterview_19 |   .0354488   .0398381     0.89   0.374    -.0429066    .1138041
_Iinterview_20 |   .0025802   .0414991     0.06   0.950     -.079042    .0842025
_Iinterview_21 |   .0003008   .0290454     0.01   0.992     -.056827    .0574286
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |    .059941   .0288518     2.08   0.038     .0031941    .1166879
_Iinterview_24 |  -.0055371   .0270693    -0.20   0.838    -.0587782     .047704
_Iinterview_25 |  -.0081177    .049775    -0.16   0.871    -.1060174     .089782
_Iinterview_26 |   .0026821   .0418492     0.06   0.949    -.0796287    .0849929
_Iinterview_27 |  -.0103152   .0359909    -0.29   0.775    -.0811036    .0604733
_Iinterview_28 |          0  (omitted)
         _cons |   .0343479   .0301355     1.14   0.255    -.0249239    .0936196
--------------------------------------------------------------------------------

Simultaneous results for dlakhama2_4_2a, dlakhama2_4_3a

                                                  Number of obs   =       1014

                                           (Std. Err. adjusted for 161 clusters in ea)
--------------------------------------------------------------------------------------
                     |               Robust
                     |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
dlakhama2_4_2a_mean  |
           civiceduc |  -.0186829   .0100577    -1.86   0.063    -.0383957    .0010298
             hotline |  -.0003197     .01156    -0.03   0.978    -.0229768    .0223373
             verdade |   .0031332   .0130211     0.24   0.810    -.0223877    .0286541
                 pr1 |  -.0084806   .0466498    -0.18   0.856    -.0999125    .0829512
                 pr2 |  -.0149826   .0209394    -0.72   0.474    -.0560231    .0260579
                 pr3 |  -.0237478    .028421    -0.84   0.403     -.079452    .0319564
                post |   -.021579   .0139469    -1.55   0.122    -.0489145    .0057564
           post_miss |  -.0081683   .0094399    -0.87   0.387    -.0266702    .0103335
              health |  -.0009457    .008724    -0.11   0.914    -.0180443     .016153
         health_miss |  -.0147392    .013555    -1.09   0.277    -.0413065    .0118282
              police |   .0023575   .0109535     0.22   0.830    -.0191109    .0238259
         police_miss |   .0560692   .0407496     1.38   0.169    -.0237985    .1359369
                 sex |   .0147663   .0083678     1.76   0.078    -.0016343     .031167
                 age |  -.0002813   .0003315    -0.85   0.396     -.000931    .0003683
              single |  -.0023743   .0124168    -0.19   0.848    -.0267108    .0219622
               divor |   .1455196   .1200057     1.21   0.225    -.0896872    .3807264
             norelig |    .020331   .0256692     0.79   0.428    -.0299798    .0706417
             protest |   .0135589   .0099357     1.36   0.172    -.0059147    .0330325
                 com |  -.0017433   .0262325    -0.07   0.947    -.0531581    .0496716
                prof |  -.0177395   .0154683    -1.15   0.251    -.0480568    .0125778
             comform |  -.0198834   .0202914    -0.98   0.327    -.0596538     .019887
            econfood |   .0036825   .0043548     0.85   0.398    -.0048527    .0122177
               house |   -.017995   .0183818    -0.98   0.328    -.0540226    .0180327
                oven |  -.0111296    .007785    -1.43   0.153    -.0263879    .0041288
              lchang |   .0027869   .0091722     0.30   0.761    -.0151904    .0207641
              llomue |  -.0155867   .0189204    -0.82   0.410    -.0526701    .0214966
             lchuabo |   .0478807   .0280131     1.71   0.087    -.0070238    .1027853
            lchitewe |   -.050482   .0240452    -2.10   0.036    -.0976098   -.0033542
              lronga |  -.0274124   .0140789    -1.95   0.052    -.0550065    .0001816
             chitsua |   -.026809   .0153392    -1.75   0.081    -.0568732    .0032552
              living |  -.0034982   .0047009    -0.74   0.457    -.0127117    .0057153
       _Iinterview_2 |   .0108234   .0110709     0.98   0.328    -.0108752     .032522
       _Iinterview_3 |   .0191238   .0283667     0.67   0.500    -.0364739    .0747216
       _Iinterview_4 |   .0178839   .0246282     0.73   0.468    -.0303865    .0661543
       _Iinterview_5 |   .0098757   .0270903     0.36   0.715    -.0432203    .0629716
       _Iinterview_6 |   .0136368   .0292486     0.47   0.641    -.0436894     .070963
       _Iinterview_7 |   .0412123   .0251237     1.64   0.101    -.0080293    .0904539
       _Iinterview_8 |    .025301   .0284819     0.89   0.374    -.0305225    .0811245
       _Iinterview_9 |   .0191937   .0187268     1.02   0.305    -.0175102    .0558975
      _Iinterview_10 |   .0314721   .0255216     1.23   0.218    -.0185494    .0814936
      _Iinterview_11 |   .0446468   .0319243     1.40   0.162    -.0179237    .1072173
      _Iinterview_12 |   .0567754   .0475614     1.19   0.233    -.0364432    .1499941
      _Iinterview_13 |   .0146223   .0209243     0.70   0.485    -.0263885    .0556331
      _Iinterview_14 |   .0588493   .0389756     1.51   0.131    -.0175415    .1352401
      _Iinterview_15 |  -.0048227   .0407031    -0.12   0.906    -.0845993    .0749538
      _Iinterview_16 |  -.0104651   .0396363    -0.26   0.792    -.0881508    .0672206
      _Iinterview_17 |   .0511911   .0472528     1.08   0.279    -.0414227     .143805
      _Iinterview_18 |  -.0131297    .040026    -0.33   0.743    -.0915791    .0653198
      _Iinterview_19 |   .0330984   .0499853     0.66   0.508    -.0648709    .1310678
      _Iinterview_20 |    .048853   .0427289     1.14   0.253    -.0348941    .1326001
      _Iinterview_21 |   .0005703   .0078408     0.07   0.942    -.0147974    .0159381
      _Iinterview_22 |          0  (omitted)
      _Iinterview_23 |   .0215552   .0267423     0.81   0.420    -.0308588    .0739692
      _Iinterview_24 |  -.0021069   .0058842    -0.36   0.720    -.0136397     .009426
      _Iinterview_25 |    .002125   .0157293     0.14   0.893    -.0287038    .0329538
      _Iinterview_26 |   .0565903   .0536039     1.06   0.291    -.0484714    .1616521
      _Iinterview_27 |  -.0030707   .0085887    -0.36   0.721    -.0199043    .0137629
      _Iinterview_28 |  -.0388518   .0458228    -0.85   0.397    -.1286629    .0509592
               _cons |   .0312154   .0273005     1.14   0.253    -.0222925    .0847233
---------------------+----------------------------------------------------------------
dlakhama2_4_2a_lnvar |
               _cons |   -4.22245    .243174   -17.36   0.000    -4.699062   -3.745837
---------------------+----------------------------------------------------------------
dlakhama2_4_3a_mean  |
           civiceduc |  -.0205165   .0117337    -1.75   0.080    -.0435141    .0024811
             hotline |  -.0150281   .0086248    -1.74   0.081    -.0319324    .0018763
             verdade |  -.0155716   .0113419    -1.37   0.170    -.0378013    .0066581
                 pr1 |  -.0209999   .0299319    -0.70   0.483    -.0796654    .0376657
                 pr2 |   .0148146    .014247     1.04   0.298     -.013109    .0427382
                 pr3 |   .0214149   .0397287     0.54   0.590    -.0564519    .0992817
                post |  -.0269647   .0189029    -1.43   0.154    -.0640137    .0100843
           post_miss |   -.011391   .0074796    -1.52   0.128    -.0260507    .0032687
              health |  -.0092785   .0155748    -0.60   0.551    -.0398045    .0212474
         health_miss |  -.0251081   .0239965    -1.05   0.295    -.0721403    .0219242
              police |   .0174996   .0167436     1.05   0.296    -.0153172    .0503164
         police_miss |   -.009845   .0283897    -0.35   0.729    -.0654877    .0457977
                 sex |   .0052809   .0079348     0.67   0.506     -.010271    .0208328
                 age |  -.0000253   .0002285    -0.11   0.912     -.000473    .0004225
              single |  -.0122675   .0089944    -1.36   0.173    -.0298961    .0053612
               divor |  -.0067381   .0089073    -0.76   0.449    -.0241961      .01072
             norelig |  -.0229314   .0188626    -1.22   0.224    -.0599015    .0140387
             protest |  -.0107939   .0107696    -1.00   0.316    -.0319019    .0103142
                 com |  -.0051602   .0078835    -0.65   0.513    -.0206116    .0102912
                prof |   .0166444   .0143671     1.16   0.247    -.0115146    .0448034
             comform |  -.0035051   .0161684    -0.22   0.828    -.0351946    .0281843
            econfood |   .0018746   .0044929     0.42   0.677    -.0069314    .0106806
               house |  -.0080601   .0159829    -0.50   0.614    -.0393861    .0232658
                oven |   -.009847    .009548    -1.03   0.302    -.0285607    .0088668
              lchang |   .0038386   .0084448     0.45   0.649    -.0127129    .0203901
              llomue |  -.0021782   .0258006    -0.08   0.933    -.0527465    .0483901
             lchuabo |   .0528474   .0402084     1.31   0.189    -.0259596    .1316545
            lchitewe |   .0110068   .0324497     0.34   0.734    -.0525935    .0746071
              lronga |  -.0152503   .0168142    -0.91   0.364    -.0482056     .017705
             chitsua |  -.0022572   .0131029    -0.17   0.863    -.0279384    .0234241
              living |  -.0044786   .0042515    -1.05   0.292    -.0128114    .0038543
       _Iinterview_2 |  -.0066041    .012563    -0.53   0.599    -.0312272     .018019
       _Iinterview_3 |  -.0101813   .0366998    -0.28   0.781    -.0821117     .061749
       _Iinterview_4 |  -.0068826   .0301913    -0.23   0.820    -.0660566    .0522913
       _Iinterview_5 |          0  (omitted)
       _Iinterview_6 |  -.0191907    .047356    -0.41   0.685    -.1120067    .0736253
       _Iinterview_7 |  -.0163124   .0336155    -0.49   0.627    -.0821975    .0495727
       _Iinterview_8 |  -.0112513   .0343127    -0.33   0.743    -.0785029    .0560003
       _Iinterview_9 |  -.0054448   .0133227    -0.41   0.683    -.0315569    .0206673
      _Iinterview_10 |   .0132922    .016767     0.79   0.428    -.0195705    .0461548
      _Iinterview_11 |  -.0033814   .0131784    -0.26   0.797    -.0292106    .0224477
      _Iinterview_12 |  -.0042545   .0125927    -0.34   0.735    -.0289357    .0204267
      _Iinterview_13 |   .0071281    .009059     0.79   0.431    -.0106271    .0248834
      _Iinterview_14 |   .0582245   .0507617     1.15   0.251    -.0412666    .1577156
      _Iinterview_15 |          0  (omitted)
      _Iinterview_16 |   -.008508   .0134895    -0.63   0.528     -.034947     .017931
      _Iinterview_17 |    .001428   .0131542     0.11   0.914    -.0243537    .0272096
      _Iinterview_18 |   -.008638   .0141182    -0.61   0.541    -.0363092    .0190331
      _Iinterview_19 |   .0354488   .0400216     0.89   0.376    -.0429921    .1138897
      _Iinterview_20 |   .0025802   .0140892     0.18   0.855    -.0250342    .0301947
      _Iinterview_21 |   .0003008   .0079421     0.04   0.970    -.0152654    .0158671
      _Iinterview_22 |          0  (omitted)
      _Iinterview_23 |    .059941   .0582199     1.03   0.303    -.0541679      .17405
      _Iinterview_24 |  -.0055371   .0050291    -1.10   0.271    -.0153941    .0043198
      _Iinterview_25 |  -.0081177   .0150774    -0.54   0.590    -.0376688    .0214334
      _Iinterview_26 |   .0026821   .0097819     0.27   0.784    -.0164901    .0218543
      _Iinterview_27 |  -.0103152   .0103315    -1.00   0.318    -.0305644    .0099341
      _Iinterview_28 |          0  (omitted)
               _cons |   .0343479   .0325027     1.06   0.291    -.0293562    .0980519
---------------------+----------------------------------------------------------------
dlakhama2_4_3a_lnvar |
               _cons |  -4.861784   .4402216   -11.04   0.000    -5.724603   -3.998966
--------------------------------------------------------------------------------------

 ( 1)  [dlakhama2_4_2a_mean]civiceduc - [dlakhama2_4_3a_mean]civiceduc = 0

           chi2(  1) =    0.05
         Prob > chi2 =    0.8225
.82247976

 ( 1)  [dlakhama2_4_2a_mean]hotline - [dlakhama2_4_3a_mean]hotline = 0

           chi2(  1) =    2.07
         Prob > chi2 =    0.1506
.15061857

 ( 1)  [dlakhama2_4_2a_mean]verdade - [dlakhama2_4_3a_mean]verdade = 0

           chi2(  1) =    2.02
         Prob > chi2 =    0.1548
.15483958

. 
. matrix define means=(m_dlakhama2_2_1, m_dlakhama2_3_1, m_dlakhama2_4_1 \ t_dlakhama2_2_1_1, t_
> dlakhama2_3_1_1, t_dlakhama2_4_1_1 \ t_dlakhama2_2_1_2, t_dlakhama2_3_1_2, t_dlakhama2_4_1_2 \
>  t_dlakhama2_2_1_3, t_dlakhama2_3_1_3, t_dlakhama2_4_1_3 \ t_dlakhama2_2_1_4, t_dlakhama2_3_1_
> 4, t_dlakhama2_4_1_4 \ t_dlakhama2_2_5, t_dlakhama2_3_5, t_dlakhama2_4_5 \ t_dlakhama2_2_6, t_
> dlakhama2_3_6, t_dlakhama2_4_6 \ t_dlakhama2_2_7, t_dlakhama2_3_7, t_dlakhama2_4_7)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_voting.xml") append sheet("voting
>  2") 


note: results saved to outputregs_voting.xml

. xml_tab $list2, save("outputregs_voting.xml") append sheet("voting 2 stats") 


note: results saved to outputregs_voting.xml

. estimates clear

. 
. global list1=""

. global list2=""

. 
. foreach i in $voting3 {
  2. 
.         regress `i' $treat $prov if time==1, cluster(ea)
  3.         estimates store `i'_2_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2_1=r(mean)
  6.         display m_`i'_2_1
  7.         test civiceduc = hotline
  8.         scalar define t_`i'_2_1_1=r(p)
  9.         display t_`i'_2_1_1
 10.         test civiceduc = verdade
 11.         scalar define t_`i'_2_1_2=r(p)
 12.         display t_`i'_2_1_2
 13.         test hotline = verdade
 14.         scalar define t_`i'_2_1_3=r(p)
 15.         display t_`i'_2_1_3
 16.         test civiceduc hotline verdade
 17.         scalar define t_`i'_2_1_4=r(p)
 18.         display t_`i'_2_1_4
 19. 
.         regress `i' $treat $prov if time==1 & lazy==0, cluster(ea)
 20.         estimates store `i'_2_2
 21.         regress `i' $treat $prov if time==1 & lazy==0
 22.         estimates store `i'_2_2a
 23.         
.         regress `i' $treat $prov if time==1 & (lazy==1|control==1), cluster(ea)
 24.         estimates store `i'_2_3
 25.         regress `i' $treat $prov if time==1 & (lazy==1|control==1)
 26.         estimates store `i'_2_3a
 27. 
.         suest `i'_2_2a `i'_2_3a, cluster(ea)
 28.         test [`i'_2_2a_mean]civiceduc=[`i'_2_3a_mean]civiceduc  
 29.         scalar define t_`i'_2_5=r(p)
 30.         display t_`i'_2_5
 31.         test [`i'_2_2a_mean]hotline=[`i'_2_3a_mean]hotline      
 32.         scalar define t_`i'_2_6=r(p)
 33.         display t_`i'_2_6
 34.         test [`i'_2_2a_mean]verdade=[`i'_2_3a_mean]verdade
 35.         scalar define t_`i'_2_7=r(p)
 36.         display t_`i'_2_7
 37. 
.         regress `i' $treat $prov $ea $controls if time==1, cluster(ea)
 38.         estimates store `i'_3_1
 39.         sum `i' if e(sample) & control == 1
 40.         scalar define m_`i'_3_1=r(mean)
 41.         display m_`i'_3_1
 42.         test civiceduc = hotline
 43.         scalar define t_`i'_3_1_1=r(p)
 44.         display t_`i'_3_1_1
 45.         test civiceduc = verdade
 46.         scalar define t_`i'_3_1_2=r(p)
 47.         display t_`i'_3_1_2
 48.         test hotline = verdade
 49.         scalar define t_`i'_3_1_3=r(p)
 50.         display t_`i'_3_1_3
 51.         test civiceduc hotline verdade
 52.         scalar define t_`i'_3_1_4=r(p)
 53.         display t_`i'_3_1_4
 54. 
.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0, cluster(ea)
 55.         estimates store `i'_3_2
 56.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0
 57.         estimates store `i'_3_2a
 58.         
.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1), cluster(ea)
 59.         estimates store `i'_3_3
 60.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1)
 61.         estimates store `i'_3_3a
 62. 
.         suest `i'_3_2a `i'_3_3a, cluster(ea)
 63.         test [`i'_3_2a_mean]civiceduc=[`i'_3_3a_mean]civiceduc  
 64.         scalar define t_`i'_3_5=r(p)
 65.         display t_`i'_3_5
 66.         test [`i'_3_2a_mean]hotline=[`i'_3_3a_mean]hotline      
 67.         scalar define t_`i'_3_6=r(p)
 68.         display t_`i'_3_6
 69.         test [`i'_3_2a_mean]verdade=[`i'_3_3a_mean]verdade
 70.         scalar define t_`i'_3_7=r(p)
 71.         display t_`i'_3_7
 72. 
.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1, cluster(ea)
 73.         estimates store `i'_4_1
 74.         sum `i' if e(sample) & control == 1
 75.         scalar define m_`i'_4_1=r(mean)
 76.         display m_`i'_4_1
 77.         test civiceduc = hotline
 78.         scalar define t_`i'_4_1_1=r(p)
 79.         display t_`i'_4_1_1
 80.         test civiceduc = verdade
 81.         scalar define t_`i'_4_1_2=r(p)
 82.         display t_`i'_4_1_2
 83.         test hotline = verdade
 84.         scalar define t_`i'_4_1_3=r(p)
 85.         display t_`i'_4_1_3
 86.         test civiceduc hotline verdade
 87.         scalar define t_`i'_4_1_4=r(p)
 88.         display t_`i'_4_1_4
 89. 
.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & lazy==0, cluster
> (ea)
 90.         estimates store `i'_4_2
 91.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & lazy==0
 92.         estimates store `i'_4_2a
 93.         
.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & (lazy==1|control
> ==1), cluster(ea)
 94.         estimates store `i'_4_3
 95.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & (lazy==1|cont
> rol==1)
 96.         estimates store `i'_4_3a
 97. 
.         suest `i'_4_2a `i'_4_3a, cluster(ea)
 98.         test [`i'_4_2a_mean]civiceduc=[`i'_4_3a_mean]civiceduc  
 99.         scalar define t_`i'_4_5=r(p)
100.         display t_`i'_4_5
101.         test [`i'_4_2a_mean]hotline=[`i'_4_3a_mean]hotline      
102.         scalar define t_`i'_4_6=r(p)
103.         display t_`i'_4_6
104.         test [`i'_4_2a_mean]verdade=[`i'_4_3a_mean]verdade
105.         scalar define t_`i'_4_7=r(p)
106.         display t_`i'_4_7
107.         
.         global list1="$list1" + " `i'_2_1" + " `i'_3_1" + " `i'_4_1" + " `i'_2_2" + " `i'_3_2"
>  + " `i'_4_2"  + " `i'_2_3" + " `i'_3_3" + " `i'_4_3"
108.         
.         }

Linear regression                                      Number of obs =    1031
                                                       F(  6,   160) =    2.01
                                                       Prob > F      =  0.0672
                                                       R-squared     =  0.0132
                                                       Root MSE      =  .16481

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0093576   .0160692     0.58   0.561    -.0223774    .0410926
     hotline |   .0041485   .0138614     0.30   0.765    -.0232265    .0315235
     verdade |  -.0129449   .0131625    -0.98   0.327    -.0389396    .0130498
         pr1 |   .0449398   .0151513     2.97   0.003     .0150175     .074862
         pr2 |   .0145339   .0110182     1.32   0.189    -.0072258    .0362937
         pr3 |   .0071696   .0101134     0.71   0.479    -.0128034    .0271427
       _cons |   .0108504   .0104644     1.04   0.301    -.0098158    .0315165
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    simango2 |       249    .0281124     .165627          0          1
.02811245

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.13
            Prob > F =    0.7195
.71952892

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    2.61
            Prob > F =    0.1081
.10805719

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    2.36
            Prob > F =    0.1267
.12671431

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.26
            Prob > F =    0.2914
.29138726

Linear regression                                      Number of obs =     872
                                                       F(  6,   160) =    1.70
                                                       Prob > F      =  0.1235
                                                       R-squared     =  0.0087
                                                       Root MSE      =  .15676

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0084434   .0161269     0.52   0.601    -.0234058    .0402925
     hotline |  -.0076274   .0142538    -0.54   0.593    -.0357772    .0205225
     verdade |  -.0136519   .0138172    -0.99   0.325    -.0409394    .0136357
         pr1 |   .0336859    .015007     2.24   0.026     .0040486    .0633232
         pr2 |   .0158684   .0115279     1.38   0.171    -.0068981    .0386349
         pr3 |   .0135111   .0113961     1.19   0.238    -.0089951    .0360174
       _cons |   .0119431   .0100223     1.19   0.235    -.0078499    .0317361
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     872
-------------+------------------------------           F(  6,   865) =    1.27
       Model |  .187540778     6  .031256796           Prob > F      =  0.2677
    Residual |  21.2574134   865  .024575044           R-squared     =  0.0087
-------------+------------------------------           Adj R-squared =  0.0019
       Total |  21.4449541   871  .024621072           Root MSE      =  .15676

------------------------------------------------------------------------------
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0084434   .0145057     0.58   0.561    -.0200271    .0369138
     hotline |  -.0076274   .0147925    -0.52   0.606    -.0366608     .021406
     verdade |  -.0136519   .0149426    -0.91   0.361    -.0429798    .0156761
         pr1 |   .0336859   .0146852     2.29   0.022      .004863    .0625088
         pr2 |   .0158684   .0152846     1.04   0.299    -.0141308    .0458676
         pr3 |   .0135111   .0150089     0.90   0.368     -.015947    .0429693
       _cons |   .0119431   .0135424     0.88   0.378    -.0146367    .0385229
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     408
                                                       F(  6,   144) =    1.67
                                                       Prob > F      =  0.1328
                                                       R-squared     =  0.0347
                                                       Root MSE      =  .18041

                                   (Std. Err. adjusted for 145 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .015189   .0284199     0.53   0.594     -.040985    .0713631
     hotline |   .0490605   .0346972     1.41   0.160    -.0195211    .1176422
     verdade |  -.0072768   .0218277    -0.33   0.739    -.0504208    .0358673
         pr1 |   .0713569   .0311684     2.29   0.024     .0097501    .1329636
         pr2 |   .0382123   .0210265     1.82   0.071    -.0033481    .0797727
         pr3 |  -.0013856   .0140435    -0.10   0.922    -.0291436    .0263725
       _cons |   .0005207   .0107649     0.05   0.961    -.0207569    .0217984
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     408
-------------+------------------------------           F(  6,   401) =    2.40
       Model |  .468560181     6  .078093364           Prob > F      =  0.0273
    Residual |  13.0510477   401  .032546254           R-squared     =  0.0347
-------------+------------------------------           Adj R-squared =  0.0202
       Total |  13.5196078   407   .03321771           Root MSE      =  .18041

------------------------------------------------------------------------------
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .015189   .0275449     0.55   0.582    -.0389614    .0693394
     hotline |   .0490605   .0265944     1.84   0.066    -.0032213    .1013423
     verdade |  -.0072768   .0279828    -0.26   0.795    -.0622881    .0477346
         pr1 |   .0713569   .0250709     2.85   0.005       .02207    .1206437
         pr2 |   .0382123   .0259108     1.47   0.141    -.0127257    .0891503
         pr3 |  -.0013856   .0245722    -0.06   0.955     -.049692    .0469209
       _cons |   .0005207     .01918     0.03   0.978    -.0371851    .0382266
------------------------------------------------------------------------------

Simultaneous results for simango2_2_2a, simango2_2_3a

                                                  Number of obs   =       1031

                                          (Std. Err. adjusted for 161 clusters in ea)
-------------------------------------------------------------------------------------
                    |               Robust
                    |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
simango2_2_2a_mean  |
          civiceduc |   .0084434   .0160713     0.53   0.599    -.0230558    .0399425
            hotline |  -.0076274   .0142046    -0.54   0.591    -.0354679    .0202131
            verdade |  -.0136519   .0137695    -0.99   0.321    -.0406396    .0133359
                pr1 |   .0336859   .0149552     2.25   0.024     .0043743    .0629975
                pr2 |   .0158684   .0114881     1.38   0.167    -.0066479    .0383847
                pr3 |   .0135111   .0113568     1.19   0.234    -.0087478    .0357701
              _cons |   .0119431   .0099877     1.20   0.232    -.0076324    .0315186
--------------------+----------------------------------------------------------------
simango2_2_2a_lnvar |
              _cons |  -3.706024   .1992219   -18.60   0.000    -4.096491   -3.315556
--------------------+----------------------------------------------------------------
simango2_2_3a_mean  |
          civiceduc |    .015189   .0281999     0.54   0.590    -.0400818    .0704598
            hotline |   .0490605   .0344286     1.42   0.154    -.0184183    .1165394
            verdade |  -.0072768   .0216587    -0.34   0.737    -.0497271    .0351735
                pr1 |   .0713569   .0309272     2.31   0.021     .0107407     .131973
                pr2 |   .0382123   .0208637     1.83   0.067    -.0026798    .0791044
                pr3 |  -.0013856   .0139348    -0.10   0.921    -.0286973    .0259261
              _cons |   .0005207   .0106816     0.05   0.961    -.0204147    .0214562
--------------------+----------------------------------------------------------------
simango2_2_3a_lnvar |
              _cons |  -3.425093   .2480237   -13.81   0.000    -3.911211   -2.938975
-------------------------------------------------------------------------------------

 ( 1)  [simango2_2_2a_mean]civiceduc - [simango2_2_3a_mean]civiceduc = 0

           chi2(  1) =    0.06
         Prob > chi2 =    0.8019
.8019021

 ( 1)  [simango2_2_2a_mean]hotline - [simango2_2_3a_mean]hotline = 0

           chi2(  1) =    2.37
         Prob > chi2 =    0.1238
.12384292

 ( 1)  [simango2_2_2a_mean]verdade - [simango2_2_3a_mean]verdade = 0

           chi2(  1) =    0.08
         Prob > chi2 =    0.7732
.77315642

Linear regression                                      Number of obs =    1017
                                                       F( 31,   160) =    1.29
                                                       Prob > F      =  0.1595
                                                       R-squared     =  0.0572
                                                       Root MSE      =  .16421

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .009244   .0163527     0.57   0.573    -.0230509     .041539
     hotline |  -.0011771   .0127896    -0.09   0.927    -.0264353    .0240812
     verdade |  -.0210778   .0140055    -1.50   0.134    -.0487373    .0065817
         pr1 |   .0356462   .0236843     1.51   0.134     -.011128    .0824203
         pr2 |   .0350328   .0386546     0.91   0.366    -.0413063     .111372
         pr3 |   .0328178   .0406301     0.81   0.420    -.0474226    .1130582
        post |  -.0004181   .0212117    -0.02   0.984    -.0423092     .041473
   post_miss |  -.0233774   .0112895    -2.07   0.040    -.0456731   -.0010817
      health |  -.0118684   .0115331    -1.03   0.305    -.0346451    .0109083
 health_miss |  -.0463125   .0175496    -2.64   0.009    -.0809712   -.0116537
      police |   .0106488   .0141748     0.75   0.454    -.0173451    .0386426
 police_miss |   .0440424   .0284117     1.55   0.123    -.0120679    .1001528
         sex |   .0353146    .012364     2.86   0.005     .0108969    .0597323
         age |  -.0008771   .0004203    -2.09   0.038    -.0017071   -.0000471
      single |  -.0186815   .0117811    -1.59   0.115     -.041948    .0045849
       divor |   -.008866   .0128351    -0.69   0.491    -.0342141    .0164822
     norelig |   .0233987   .0381347     0.61   0.540    -.0519134    .0987109
     protest |  -.0006221   .0126868    -0.05   0.961    -.0256773     .024433
         com |  -.0181781    .017406    -1.04   0.298    -.0525532    .0161971
        prof |   .0909678   .0846427     1.07   0.284    -.0761932    .2581289
     comform |  -.0382224     .01315    -2.91   0.004    -.0641923   -.0122525
    econfood |   .0021777   .0046764     0.47   0.642    -.0070578    .0114131
       house |   .0056005   .0140346     0.40   0.690    -.0221165    .0333175
        oven |   .0381732   .0238346     1.60   0.111    -.0088978    .0852441
      lchang |  -.0167973   .0370872    -0.45   0.651    -.0900409    .0564463
      llomue |  -.0085658   .0331267    -0.26   0.796    -.0739878    .0568563
     lchuabo |   .0120219   .0254178     0.47   0.637    -.0381758    .0622195
    lchitewe |   .1891681   .1123693     1.68   0.094    -.0327502    .4110864
      lronga |  -.0093566   .0199792    -0.47   0.640    -.0488135    .0301003
     chitsua |   -.039331   .0181732    -2.16   0.032    -.0752211   -.0034408
      living |   .0010378   .0055466     0.19   0.852    -.0099162    .0119919
       _cons |   .0249434   .0232723     1.07   0.285    -.0210172     .070904
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    simango2 |       247    .0283401    .1662795          0          1
.02834008

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.49
            Prob > F =    0.4831
.48311907

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    4.13
            Prob > F =    0.0437
.04370714

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    2.88
            Prob > F =    0.0917
.0916967

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.75
            Prob > F =    0.1582
.15816418

Linear regression                                      Number of obs =     862
                                                       F( 31,   160) =    0.79
                                                       Prob > F      =  0.7751
                                                       R-squared     =  0.0464
                                                       Root MSE      =  .15695

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0089185   .0157198     0.57   0.571    -.0221265    .0399635
     hotline |  -.0083423   .0133386    -0.63   0.533    -.0346848    .0180002
     verdade |  -.0186926   .0140876    -1.33   0.186    -.0465143     .009129
         pr1 |   .0145515   .0264988     0.55   0.584     -.037781    .0668841
         pr2 |   .0523127   .0430604     1.21   0.226    -.0327273    .1373528
         pr3 |   .0627058   .0447247     1.40   0.163     -.025621    .1510327
        post |   .0024864   .0240037     0.10   0.918    -.0449185    .0498913
   post_miss |  -.0209499   .0125645    -1.67   0.097    -.0457636    .0038638
      health |  -.0043334   .0127554    -0.34   0.735     -.029524    .0208572
 health_miss |  -.0354725   .0188083    -1.89   0.061     -.072617    .0016719
      police |   .0048581   .0153163     0.32   0.752      -.02539    .0351063
 police_miss |   .0358029    .030676     1.17   0.245    -.0247792    .0963849
         sex |   .0320709   .0119297     2.69   0.008      .008511    .0556309
         age |  -.0009791   .0004112    -2.38   0.018    -.0017912   -.0001669
      single |  -.0154709   .0120869    -1.28   0.202    -.0393413    .0083994
       divor |  -.0145641   .0167325    -0.87   0.385    -.0476092     .018481
     norelig |    .013362   .0402672     0.33   0.740    -.0661619    .0928858
     protest |  -.0001677   .0144359    -0.01   0.991    -.0286772    .0283419
         com |  -.0318192   .0090706    -3.51   0.001    -.0497328   -.0139056
        prof |   .0504194   .0754825     0.67   0.505    -.0986512      .19949
     comform |  -.0384803   .0146439    -2.63   0.009    -.0674005   -.0095601
    econfood |   .0022415   .0047395     0.47   0.637    -.0071185    .0116015
       house |   .0089999   .0142564     0.63   0.529     -.019155    .0371548
        oven |   .0439295   .0270853     1.62   0.107    -.0095613    .0974203
      lchang |  -.0441185   .0386472    -1.14   0.255    -.1204429     .032206
      llomue |  -.0101692   .0322684    -0.32   0.753    -.0738962    .0535577
     lchuabo |   .0453555   .0288447     1.57   0.118      -.01161     .102321
    lchitewe |     .07685   .1377441     0.56   0.578     -.195181     .348881
      lronga |   .0018662   .0243533     0.08   0.939    -.0462292    .0499616
     chitsua |  -.0283218   .0202384    -1.40   0.164    -.0682907    .0116471
      living |   .0003831   .0051741     0.07   0.941    -.0098352    .0106013
       _cons |   .0265067   .0245319     1.08   0.282    -.0219414    .0749548
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     862
-------------+------------------------------           F( 31,   830) =    1.30
       Model |  .994126529    31  .032068598           Prob > F      =  0.1270
    Residual |  20.4443886   830  .024631793           R-squared     =  0.0464
-------------+------------------------------           Adj R-squared =  0.0108
       Total |  21.4385151   861  .024899553           Root MSE      =  .15695

------------------------------------------------------------------------------
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0089185   .0155404     0.57   0.566    -.0215845    .0394215
     hotline |  -.0083423   .0154154    -0.54   0.589       -.0386    .0219154
     verdade |  -.0186926   .0159906    -1.17   0.243    -.0500795    .0126942
         pr1 |   .0145515    .023765     0.61   0.541     -.032095    .0611981
         pr2 |   .0523127   .0322444     1.62   0.105    -.0109775    .1156029
         pr3 |   .0627058   .0332075     1.89   0.059    -.0024747    .1278863
        post |   .0024864   .0221762     0.11   0.911    -.0410417    .0460144
   post_miss |  -.0209499   .0372008    -0.56   0.573    -.0939687    .0520688
      health |  -.0043334   .0133218    -0.33   0.745    -.0304819     .021815
 health_miss |  -.0354725   .0433378    -0.82   0.413     -.120537     .049592
      police |   .0048581   .0164477     0.30   0.768    -.0274258    .0371421
 police_miss |   .0358029   .0681442     0.53   0.599    -.0979524    .1695581
         sex |   .0320709   .0115315     2.78   0.006     .0094366    .0547052
         age |  -.0009791   .0004464    -2.19   0.029    -.0018553   -.0001028
      single |  -.0154709    .015108    -1.02   0.306    -.0451253    .0141834
       divor |  -.0145641   .0607573    -0.24   0.811      -.13382    .1046919
     norelig |    .013362   .0282058     0.47   0.636    -.0420012    .0687251
     protest |  -.0001677   .0136989    -0.01   0.990    -.0270562    .0267209
         com |  -.0318192     .02538    -1.25   0.210    -.0816357    .0179973
        prof |   .0504194   .0449669     1.12   0.263    -.0378427    .1386816
     comform |  -.0384803   .0538765    -0.71   0.475    -.1442304    .0672698
    econfood |   .0022415   .0048158     0.47   0.642    -.0072111    .0116941
       house |   .0089999   .0165933     0.54   0.588    -.0235699    .0415698
        oven |   .0439295   .0214083     2.05   0.040     .0019088    .0859502
      lchang |  -.0441185   .0286127    -1.54   0.123    -.1002803    .0120434
      llomue |  -.0101692   .0232123    -0.44   0.661    -.0557309    .0353925
     lchuabo |   .0453555    .021983     2.06   0.039     .0022068    .0885042
    lchitewe |     .07685   .0614755     1.25   0.212    -.0438157    .1975157
      lronga |   .0018662   .0230856     0.08   0.936    -.0434468    .0471792
     chitsua |  -.0283218   .0548186    -0.52   0.606    -.1359211    .0792775
      living |   .0003831   .0054025     0.07   0.943    -.0102211    .0109873
       _cons |   .0265067   .0292636     0.91   0.365    -.0309327     .083946
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     402
                                                       F( 31,   142) =    0.77
                                                       Prob > F      =  0.8016
                                                       R-squared     =  0.1274
                                                       Root MSE      =  .17851

                                   (Std. Err. adjusted for 143 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0248577   .0341298     0.73   0.468    -.0426105    .0923259
     hotline |   .0243324   .0241495     1.01   0.315    -.0234066    .0720714
     verdade |  -.0240151    .031419    -0.76   0.446    -.0861245    .0380944
         pr1 |   .0559502   .0498345     1.12   0.263    -.0425632    .1544636
         pr2 |   .0134479   .0613982     0.22   0.827    -.1079247    .1348205
         pr3 |  -.0128711   .0602789    -0.21   0.831    -.1320312     .106289
        post |   .0067691   .0345285     0.20   0.845    -.0614872    .0750253
   post_miss |  -.0063465   .0209204    -0.30   0.762    -.0477022    .0350093
      health |  -.0007286   .0203988    -0.04   0.972    -.0410532     .039596
 health_miss |   -.031326    .037095    -0.84   0.400    -.1046558    .0420038
      police |   .0116404   .0275254     0.42   0.673    -.0427721    .0660529
 police_miss |  -.0165853   .0597083    -0.28   0.782    -.1346174    .1014467
         sex |   .0575751   .0210246     2.74   0.007     .0160135    .0991366
         age |  -.0010793   .0008389    -1.29   0.200    -.0027376     .000579
      single |  -.0194044   .0225519    -0.86   0.391    -.0639853    .0251765
       divor |  -.0079549   .0435592    -0.18   0.855    -.0940631    .0781534
     norelig |   .0970086    .090085     1.08   0.283    -.0810723    .2750896
     protest |  -.0108228   .0242365    -0.45   0.656    -.0587338    .0370882
         com |  -.0023892   .0391656    -0.06   0.951    -.0798121    .0750338
        prof |   .0755071    .096693     0.78   0.436    -.1156367    .2666509
     comform |  -.0385529   .0324713    -1.19   0.237    -.1027424    .0256367
    econfood |   .0095213   .0079965     1.19   0.236    -.0062862    .0253288
       house |   .0140042    .028445     0.49   0.623    -.0422263    .0702347
        oven |   .0647835   .0447318     1.45   0.150    -.0236428    .1532098
      lchang |   .0275771   .0559372     0.49   0.623    -.0830003    .1381544
      llomue |   .0126636   .0621097     0.20   0.839    -.1101154    .1354427
     lchuabo |   .0016118   .0547229     0.03   0.977    -.1065651    .1097887
    lchitewe |   .3246275   .1981343     1.64   0.104    -.0670465    .7163015
      lronga |   .0068198   .0437766     0.16   0.876    -.0797182    .0933578
     chitsua |  -.0422466   .0305153    -1.38   0.168    -.1025696    .0180764
      living |   .0024782   .0096554     0.26   0.798    -.0166087    .0215651
       _cons |  -.0198505   .0374119    -0.53   0.597    -.0938068    .0541058
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     402
-------------+------------------------------           F( 31,   370) =    1.74
       Model |  1.72200586    31  .055548576           Prob > F      =  0.0096
    Residual |   11.790432   370  .031866032           R-squared     =  0.1274
-------------+------------------------------           Adj R-squared =  0.0543
       Total |  13.5124378   401  .033696852           Root MSE      =  .17851

------------------------------------------------------------------------------
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0248577   .0297284     0.84   0.404    -.0336001    .0833155
     hotline |   .0243324   .0279494     0.87   0.385    -.0306273     .079292
     verdade |  -.0240151     .03025    -0.79   0.428    -.0834986    .0354685
         pr1 |   .0559502   .0433458     1.29   0.198    -.0292848    .1411852
         pr2 |   .0134479   .0545134     0.25   0.805     -.093747    .1206428
         pr3 |  -.0128711    .055812    -0.23   0.818    -.1226195    .0968773
        post |   .0067691   .0332028     0.20   0.839    -.0585208    .0720589
   post_miss |  -.0063465   .0484053    -0.13   0.896    -.1015305    .0888376
      health |  -.0007286   .0236256    -0.03   0.975     -.047186    .0457288
 health_miss |   -.031326     .08566    -0.37   0.715    -.1997674    .1371154
      police |   .0116404     .02779     0.42   0.676    -.0430059    .0662866
 police_miss |  -.0165853   .1344637    -0.12   0.902    -.2809943    .2478236
         sex |   .0575751   .0193731     2.97   0.003     .0194799    .0956702
         age |  -.0010793   .0008013    -1.35   0.179     -.002655    .0004964
      single |  -.0194044   .0242098    -0.80   0.423    -.0670105    .0282017
       divor |  -.0079549   .1056526    -0.08   0.940    -.2157097       .1998
     norelig |   .0970086   .0550365     1.76   0.079     -.011215    .2052323
     protest |  -.0108228   .0248565    -0.44   0.664    -.0597005    .0380549
         com |  -.0023892   .0506452    -0.05   0.962    -.1019777    .0971994
        prof |   .0755071   .0671565     1.12   0.262    -.0565492    .2075634
     comform |  -.0385529   .0751962    -0.51   0.608    -.1864184    .1093127
    econfood |   .0095213   .0085057     1.12   0.264    -.0072044    .0262469
       house |   .0140042   .0275574     0.51   0.612    -.0401846     .068193
        oven |   .0647835   .0373915     1.73   0.084    -.0087429    .1383099
      lchang |   .0275771   .0479767     0.57   0.566    -.0667642    .1219183
      llomue |   .0126636    .042742     0.30   0.767    -.0713842    .0967114
     lchuabo |   .0016118   .0403919     0.04   0.968    -.0778148    .0810384
    lchitewe |   .3246275   .0865546     3.75   0.000     .1544268    .4948282
      lronga |   .0068198   .0371846     0.18   0.855    -.0662999    .0799395
     chitsua |  -.0422466   .1089247    -0.39   0.698    -.2564358    .1719425
      living |   .0024782   .0089735     0.28   0.783    -.0151672    .0201237
       _cons |  -.0198505   .0475617    -0.42   0.677    -.1133757    .0736747
------------------------------------------------------------------------------

Simultaneous results for simango2_3_2a, simango2_3_3a

                                                  Number of obs   =       1017

                                          (Std. Err. adjusted for 161 clusters in ea)
-------------------------------------------------------------------------------------
                    |               Robust
                    |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
simango2_3_2a_mean  |
          civiceduc |   .0089185   .0154342     0.58   0.563    -.0213319     .039169
            hotline |  -.0083423   .0130963    -0.64   0.524    -.0340106     .017326
            verdade |  -.0186926   .0138317    -1.35   0.177    -.0458022     .008417
                pr1 |   .0145515   .0260174     0.56   0.576    -.0364416    .0655447
                pr2 |   .0523127   .0422781     1.24   0.216    -.0305509    .1351763
                pr3 |   .0627058   .0439122     1.43   0.153    -.0233604     .148772
               post |   .0024864   .0235676     0.11   0.916    -.0437053     .048678
          post_miss |  -.0209499   .0123363    -1.70   0.089    -.0451286    .0032288
             health |  -.0043334   .0125236    -0.35   0.729    -.0288793    .0202125
        health_miss |  -.0354725   .0184666    -1.92   0.055    -.0716663    .0007213
             police |   .0048581    .015038     0.32   0.747    -.0246159    .0343321
        police_miss |   .0358029   .0301187     1.19   0.235    -.0232287    .0948344
                sex |   .0320709   .0117129     2.74   0.006      .009114    .0550279
                age |  -.0009791   .0004038    -2.42   0.015    -.0017704   -.0001877
             single |  -.0154709   .0118673    -1.30   0.192    -.0387304    .0077885
              divor |  -.0145641   .0164286    -0.89   0.375    -.0467634    .0176353
            norelig |    .013362   .0395357     0.34   0.735    -.0641266    .0908505
            protest |  -.0001677   .0141737    -0.01   0.991    -.0279476    .0276123
                com |  -.0318192   .0089059    -3.57   0.000    -.0492743   -.0143641
               prof |   .0504194   .0741112     0.68   0.496    -.0948359    .1956748
            comform |  -.0384803   .0143778    -2.68   0.007    -.0666603   -.0103003
           econfood |   .0022415   .0046534     0.48   0.630    -.0068789    .0113619
              house |   .0089999   .0139974     0.64   0.520    -.0184344    .0364343
               oven |   .0439295   .0265932     1.65   0.099    -.0081923    .0960513
             lchang |  -.0441185   .0379451    -1.16   0.245    -.1184895    .0302526
             llomue |  -.0101692   .0316822    -0.32   0.748    -.0722652    .0519267
            lchuabo |   .0453555   .0283207     1.60   0.109    -.0101521    .1008631
           lchitewe |     .07685   .1352416     0.57   0.570    -.1882187    .3419187
             lronga |   .0018662   .0239109     0.08   0.938    -.0449983    .0487306
            chitsua |  -.0283218   .0198708    -1.43   0.154    -.0672677    .0106242
             living |   .0003831   .0050801     0.08   0.940    -.0095737    .0103398
              _cons |   .0265067   .0240862     1.10   0.271    -.0207015    .0737148
--------------------+----------------------------------------------------------------
simango2_3_2a_lnvar |
              _cons |  -3.703717   .1865736   -19.85   0.000    -4.069395    -3.33804
--------------------+----------------------------------------------------------------
simango2_3_3a_mean  |
          civiceduc |   .0248577   .0327711     0.76   0.448    -.0393726    .0890879
            hotline |   .0243324   .0231881     1.05   0.294    -.0211156    .0697803
            verdade |  -.0240151   .0301682    -0.80   0.426    -.0831437    .0351136
                pr1 |   .0559502   .0478507     1.17   0.242    -.0378354    .1497358
                pr2 |   .0134479    .058954     0.23   0.820    -.1020998    .1289956
                pr3 |  -.0128711   .0578793    -0.22   0.824    -.1263125    .1005703
               post |   .0067691   .0331539     0.20   0.838    -.0582114    .0717496
          post_miss |  -.0063465   .0200876    -0.32   0.752    -.0457175    .0330245
             health |  -.0007286   .0195868    -0.04   0.970     -.039118    .0376608
        health_miss |   -.031326   .0356183    -0.88   0.379    -.1011366    .0384845
             police |   .0116404   .0264296     0.44   0.660    -.0401608    .0634415
        police_miss |  -.0165853   .0573314    -0.29   0.772    -.1289528    .0957821
                sex |   .0575751   .0201876     2.85   0.004     .0180081     .097142
                age |  -.0010793   .0008055    -1.34   0.180     -.002658    .0004994
             single |  -.0194044   .0216542    -0.90   0.370    -.0618458     .023037
              divor |  -.0079549   .0418251    -0.19   0.849    -.0899306    .0740209
            norelig |   .0970086   .0864988     1.12   0.262    -.0725259    .2665431
            protest |  -.0108228   .0232717    -0.47   0.642    -.0564344    .0347889
                com |  -.0023892   .0376065    -0.06   0.949    -.0760965    .0713182
               prof |   .0755071   .0928438     0.81   0.416    -.1064634    .2574776
            comform |  -.0385529   .0311786    -1.24   0.216    -.0996618    .0225561
           econfood |   .0095213   .0076781     1.24   0.215    -.0055276    .0245701
              house |   .0140042   .0273127     0.51   0.608    -.0395277    .0675361
               oven |   .0647835   .0429511     1.51   0.131    -.0193991    .1489661
             lchang |   .0275771   .0537104     0.51   0.608    -.0776935    .1328476
             llomue |   .0126636   .0596372     0.21   0.832    -.1042231    .1295503
            lchuabo |   .0016118   .0525445     0.03   0.976    -.1013735    .1045971
           lchitewe |   .3246275   .1902468     1.71   0.088    -.0482494    .6975044
             lronga |   .0068198   .0420339     0.16   0.871    -.0755651    .0892047
            chitsua |  -.0422466   .0293005    -1.44   0.149    -.0996746    .0151814
             living |   .0024782    .009271     0.27   0.789    -.0156927    .0206491
              _cons |  -.0198505   .0359226    -0.55   0.581    -.0902575    .0505565
--------------------+----------------------------------------------------------------
simango2_3_3a_lnvar |
              _cons |  -3.446215   .2271171   -15.17   0.000    -3.891356   -3.001073
-------------------------------------------------------------------------------------

 ( 1)  [simango2_3_2a_mean]civiceduc - [simango2_3_3a_mean]civiceduc = 0

           chi2(  1) =    0.26
         Prob > chi2 =    0.6124
.61236858

 ( 1)  [simango2_3_2a_mean]hotline - [simango2_3_3a_mean]hotline = 0

           chi2(  1) =    1.83
         Prob > chi2 =    0.1765
.17654846

 ( 1)  [simango2_3_2a_mean]verdade - [simango2_3_3a_mean]verdade = 0

           chi2(  1) =    0.03
         Prob > chi2 =    0.8612
.86123352
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_22 omitted because of collinearity

Linear regression                                      Number of obs =    1014
                                                       F( 54,   160) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.0754
                                                       Root MSE      =  .16506

                                     (Std. Err. adjusted for 161 clusters in ea)
--------------------------------------------------------------------------------
               |               Robust
      simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |   .0097831   .0168991     0.58   0.563    -.0235909    .0431571
       hotline |  -.0000258   .0136698    -0.00   0.998    -.0270223    .0269707
       verdade |  -.0223068   .0148759    -1.50   0.136    -.0516852    .0070716
           pr1 |   .0292002   .0324874     0.90   0.370    -.0349593    .0933596
           pr2 |  -.0022687   .0326744    -0.07   0.945    -.0667974      .06226
           pr3 |   .0666706   .0560082     1.19   0.236      -.04394    .1772812
          post |  -.0046937   .0220508    -0.21   0.832    -.0482419    .0388545
     post_miss |  -.0241703   .0145121    -1.67   0.098    -.0528303    .0044896
        health |  -.0120822   .0113399    -1.07   0.288    -.0344773    .0103129
   health_miss |  -.0513495   .0192654    -2.67   0.008    -.0893968   -.0133023
        police |   .0113589   .0141949     0.80   0.425    -.0166746    .0393923
   police_miss |   .0450021   .0309057     1.46   0.147    -.0160336    .1060378
           sex |   .0369959   .0127119     2.91   0.004      .011891    .0621007
           age |  -.0008851   .0004359    -2.03   0.044    -.0017459   -.0000243
        single |  -.0190737   .0136083    -1.40   0.163    -.0459488    .0078014
         divor |  -.0132678   .0139984    -0.95   0.345    -.0409133    .0143776
       norelig |   .0196067   .0378749     0.52   0.605    -.0551925    .0944058
       protest |  -.0019181   .0137076    -0.14   0.889    -.0289893    .0251531
           com |  -.0150069   .0180898    -0.83   0.408    -.0507324    .0207186
          prof |   .0906218   .0880624     1.03   0.305    -.0832927    .2645364
       comform |  -.0282282   .0149198    -1.89   0.060    -.0576934     .001237
      econfood |   .0059746   .0054002     1.11   0.270    -.0046902    .0166394
         house |  -.0033936   .0160198    -0.21   0.833    -.0350312    .0282439
          oven |   .0453588   .0240195     1.89   0.061    -.0020774     .092795
        lchang |  -.0162026   .0370375    -0.44   0.662     -.089348    .0569428
        llomue |  -.0103705   .0369969    -0.28   0.780    -.0834357    .0626947
       lchuabo |   .0120565   .0272957     0.44   0.659    -.0418498    .0659628
      lchitewe |    .172104   .1198708     1.44   0.153    -.0646291    .4088372
        lronga |   .0002994   .0197992     0.02   0.988     -.038802    .0394008
       chitsua |  -.0405395   .0216536    -1.87   0.063    -.0833033    .0022242
        living |   .0002259   .0056061     0.04   0.968    -.0108456    .0112974
 _Iinterview_2 |  -.0553011   .0404261    -1.37   0.173    -.1351387    .0245365
 _Iinterview_3 |  -.0947496   .0464554    -2.04   0.043    -.1864944   -.0030048
 _Iinterview_4 |  -.0684821   .0462638    -1.48   0.141    -.1598486    .0228844
 _Iinterview_5 |  -.0577087   .0428283    -1.35   0.180    -.1422904    .0268731
 _Iinterview_6 |  -.0901183   .0452297    -1.99   0.048    -.1794425   -.0007942
 _Iinterview_7 |   -.021711   .0443169    -0.49   0.625    -.1092324    .0658105
 _Iinterview_8 |  -.0930987   .0457802    -2.03   0.044    -.1835101   -.0026873
 _Iinterview_9 |   .0317726   .0465253     0.68   0.496    -.0601104    .1236555
_Iinterview_10 |  -.0294798    .036723    -0.80   0.423    -.1020041    .0430445
_Iinterview_11 |  -.0095981   .0441047    -0.22   0.828    -.0967005    .0775043
_Iinterview_12 |  -.0322434   .0405629    -0.79   0.428    -.1123512    .0478644
_Iinterview_13 |  -.0087492   .0378651    -0.23   0.818    -.0835291    .0660307
_Iinterview_14 |  -.0086832   .0434197    -0.20   0.842    -.0944328    .0770664
_Iinterview_15 |  -.0309976   .0655547    -0.47   0.637    -.1604617    .0984665
_Iinterview_16 |  -.0323022   .0598528    -0.54   0.590    -.1505057    .0859013
_Iinterview_17 |  -.0665691   .0465353    -1.43   0.155    -.1584717    .0253335
_Iinterview_18 |   -.031587    .051271    -0.62   0.539    -.1328421    .0696682
_Iinterview_19 |  -.0091913   .0536421    -0.17   0.864    -.1151291    .0967465
_Iinterview_20 |  -.0130958   .0421148    -0.31   0.756    -.0962684    .0700767
_Iinterview_21 |  -.0444333   .0330904    -1.34   0.181    -.1097835     .020917
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |  -.0483828   .0320172    -1.51   0.133    -.1116135    .0148479
_Iinterview_24 |  -.0482149   .0308722    -1.56   0.120    -.1091844    .0127547
_Iinterview_25 |  -.0357821   .0369523    -0.97   0.334    -.1087593    .0371951
_Iinterview_26 |  -.0434738   .0344803    -1.26   0.209     -.111569    .0246213
_Iinterview_27 |  -.0615577   .0341239    -1.80   0.073    -.1289491    .0058337
_Iinterview_28 |  -.0484296   .0614479    -0.79   0.432    -.1697831    .0729239
         _cons |   .0694939   .0353743     1.96   0.051    -.0003669    .1393547
--------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    simango2 |       247    .0283401    .1662795          0          1
.02834008

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.41
            Prob > F =    0.5212
.52115695

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    4.24
            Prob > F =    0.0411
.04106318

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    3.31
            Prob > F =    0.0707
.07071265

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.83
            Prob > F =    0.1442
.14423235
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_22 omitted because of collinearity

Linear regression                                      Number of obs =     860
                                                       F( 53,   160) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.0681
                                                       Root MSE      =  .15783

                                     (Std. Err. adjusted for 161 clusters in ea)
--------------------------------------------------------------------------------
               |               Robust
      simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |   .0098386   .0165654     0.59   0.553    -.0228765    .0425538
       hotline |  -.0064653   .0139242    -0.46   0.643    -.0339643    .0210337
       verdade |  -.0204776   .0151903    -1.35   0.180    -.0504769    .0095217
           pr1 |  -.0283149   .0297859    -0.95   0.343    -.0871391    .0305094
           pr2 |   .0141165   .0455571     0.31   0.757    -.0758542    .1040872
           pr3 |   .1078522   .0700319     1.54   0.126    -.0304539    .2461584
          post |  -.0013091   .0254395    -0.05   0.959    -.0515497    .0489314
     post_miss |   -.015527   .0155249    -1.00   0.319     -.046187    .0151331
        health |  -.0068027   .0121723    -0.56   0.577    -.0308417    .0172363
   health_miss |  -.0440334   .0219363    -2.01   0.046    -.0873554   -.0007115
        police |   .0067131   .0152075     0.44   0.659    -.0233202    .0367464
   police_miss |   .0355723   .0329687     1.08   0.282    -.0295375    .1006822
           sex |   .0340723    .012284     2.77   0.006     .0098125     .058332
           age |  -.0010005   .0004408    -2.27   0.025     -.001871     -.00013
        single |  -.0161922   .0140782    -1.15   0.252    -.0439953     .011611
         divor |  -.0230205   .0182848    -1.26   0.210    -.0591312    .0130903
       norelig |   .0096537   .0395279     0.24   0.807    -.0684099    .0877173
       protest |   -.003467   .0156591    -0.22   0.825    -.0343921    .0274581
           com |  -.0268219   .0103655    -2.59   0.011    -.0472928    -.006351
          prof |   .0568195   .0796152     0.71   0.476    -.1004126    .2140517
       comform |  -.0285766   .0185396    -1.54   0.125    -.0651906    .0080374
      econfood |   .0064567   .0052586     1.23   0.221    -.0039285    .0168418
         house |   .0017518   .0142668     0.12   0.902    -.0264238    .0299274
          oven |   .0503783   .0272395     1.85   0.066    -.0034171    .1041737
        lchang |  -.0407543    .039407    -1.03   0.303    -.1185792    .0370707
        llomue |  -.0137359   .0361381    -0.38   0.704    -.0851052    .0576334
       lchuabo |   .0477175   .0311186     1.53   0.127    -.0137386    .1091737
      lchitewe |    .058547   .1499259     0.39   0.697    -.2375419    .3546358
        lronga |   .0103992   .0239486     0.43   0.665    -.0368968    .0576953
       chitsua |  -.0264866      .0233    -1.14   0.257    -.0725019    .0195286
        living |  -.0007178   .0054397    -0.13   0.895    -.0114606    .0100251
 _Iinterview_2 |  -.0421208   .0440782    -0.96   0.341     -.129171    .0449294
 _Iinterview_3 |  -.1078339   .0495142    -2.18   0.031    -.2056197   -.0100481
 _Iinterview_4 |  -.0724998   .0479669    -1.51   0.133    -.1672298    .0222302
 _Iinterview_5 |  -.0736292   .0446541    -1.65   0.101    -.1618167    .0145584
 _Iinterview_6 |  -.0976529    .047101    -2.07   0.040    -.1906728    -.004633
 _Iinterview_7 |  -.0230159   .0489504    -0.47   0.639    -.1196881    .0736562
 _Iinterview_8 |  -.1064918   .0491749    -2.17   0.032    -.2036074   -.0093762
 _Iinterview_9 |   .0334018   .0471092     0.71   0.479    -.0596342    .1264378
_Iinterview_10 |  -.0248533   .0464895    -0.53   0.594    -.1166654    .0669588
_Iinterview_11 |  -.0010307   .0515382    -0.02   0.984    -.1028136    .1007522
_Iinterview_12 |  -.0182252   .0442969    -0.41   0.681    -.1057073    .0692569
_Iinterview_13 |   -.003518   .0411698    -0.09   0.932    -.0848243    .0777882
_Iinterview_14 |   .0019918   .0461668     0.04   0.966     -.089183    .0931667
_Iinterview_15 |   .0278293   .0678973     0.41   0.682    -.1062612    .1619198
_Iinterview_16 |   .0002335   .0546919     0.00   0.997    -.1077777    .1082447
_Iinterview_17 |  -.0130869   .0413781    -0.32   0.752    -.0948045    .0686307
_Iinterview_18 |   .0095434   .0471772     0.20   0.840    -.0836268    .1027137
_Iinterview_19 |    .044727   .0557969     0.80   0.424    -.0654664    .1549204
_Iinterview_20 |  -.0083316   .0412934    -0.20   0.840     -.089882    .0732187
_Iinterview_21 |  -.0371681   .0329677    -1.13   0.261     -.102276    .0279399
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |  -.0403926   .0312372    -1.29   0.198     -.102083    .0212978
_Iinterview_24 |  -.0404765   .0319426    -1.27   0.207      -.10356     .022607
_Iinterview_25 |  -.0273705   .0361439    -0.76   0.450    -.0987513    .0440102
_Iinterview_26 |  -.0396148   .0334359    -1.18   0.238    -.1056473    .0264177
_Iinterview_27 |  -.0540486   .0339578    -1.59   0.113    -.1211118    .0130146
_Iinterview_28 |  -.0195285    .053203    -0.37   0.714    -.1245992    .0855422
         _cons |   .0655036   .0371056     1.77   0.079    -.0077764    .1387836
--------------------------------------------------------------------------------
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_22 omitted because of collinearity

      Source |       SS       df       MS              Number of obs =     860
-------------+------------------------------           F( 57,   802) =    1.03
       Model |  1.45915068    57  .025599135           Prob > F      =  0.4218
    Residual |  19.9780586   802  .024910298           R-squared     =  0.0681
-------------+------------------------------           Adj R-squared =  0.0018
       Total |  21.4372093   859  .024956006           Root MSE      =  .15783

--------------------------------------------------------------------------------
      simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |   .0098386   .0159393     0.62   0.537    -.0214491    .0411264
       hotline |  -.0064653   .0157672    -0.41   0.682    -.0374152    .0244846
       verdade |  -.0204776   .0163374    -1.25   0.210    -.0525467    .0115915
           pr1 |  -.0283149   .1166633    -0.24   0.808    -.2573164    .2006866
           pr2 |   .0141165   .1658316     0.09   0.932    -.3113986    .3396316
           pr3 |   .1078522   .1840702     0.59   0.558     -.253464    .4691685
          post |  -.0013091   .0228197    -0.06   0.954    -.0461026    .0434844
     post_miss |   -.015527    .037898    -0.41   0.682     -.089918    .0588641
        health |  -.0068027   .0136132    -0.50   0.617    -.0335244     .019919
   health_miss |  -.0440334   .0443353    -0.99   0.321    -.1310603    .0429935
        police |   .0067131   .0169298     0.40   0.692    -.0265188     .039945
   police_miss |   .0355723   .0693264     0.51   0.608    -.1005104     .171655
           sex |   .0340723   .0117588     2.90   0.004     .0109907    .0571539
           age |  -.0010005   .0004573    -2.19   0.029    -.0018982   -.0001028
        single |  -.0161922   .0161084    -1.01   0.315    -.0478118    .0154275
         divor |  -.0230205   .0616352    -0.37   0.709    -.1440059    .0979649
       norelig |   .0096537   .0286315     0.34   0.736    -.0465479    .0658553
       protest |   -.003467   .0140988    -0.25   0.806    -.0311419    .0242078
           com |  -.0268219   .0261249    -1.03   0.305    -.0781033    .0244594
          prof |   .0568195   .0461134     1.23   0.218    -.0336977    .1473368
       comform |  -.0285766   .0556405    -0.51   0.608    -.1377947    .0806415
      econfood |   .0064567   .0051935     1.24   0.214    -.0037377    .0166511
         house |   .0017518   .0186935     0.09   0.925    -.0349422    .0384458
          oven |   .0503783   .0223868     2.25   0.025     .0064347    .0943219
        lchang |  -.0407543   .0296002    -1.38   0.169    -.0988573    .0173487
        llomue |  -.0137359   .0239352    -0.57   0.566     -.060719    .0332472
       lchuabo |   .0477175   .0224803     2.12   0.034     .0035904    .0918446
      lchitewe |    .058547   .0628909     0.93   0.352    -.0649032    .1819972
        lronga |   .0103992   .0246584     0.42   0.673    -.0380034    .0588019
       chitsua |  -.0264866   .0558888    -0.47   0.636    -.1361921    .0832189
        living |  -.0007178   .0055919    -0.13   0.898    -.0116943    .0102587
 _Iinterview_2 |  -.0421208   .0751872    -0.56   0.575    -.1897077    .1054661
 _Iinterview_3 |  -.1078339   .1831454    -0.59   0.556    -.4673348    .2516671
 _Iinterview_4 |  -.0724998   .1809979    -0.40   0.689    -.4277853    .2827857
 _Iinterview_5 |  -.0736292   .2411599    -0.31   0.760    -.5470084      .39975
 _Iinterview_6 |  -.0976529   .1847043    -0.53   0.597    -.4602138     .264908
 _Iinterview_7 |  -.0230159   .1817277    -0.13   0.899    -.3797341    .3337022
 _Iinterview_8 |  -.1064918   .1835848    -0.58   0.562    -.4668552    .2538715
 _Iinterview_9 |   .0334018   .1665059     0.20   0.841    -.2934371    .3602406
_Iinterview_10 |  -.0248533   .1802615    -0.14   0.890    -.3786933    .3289867
_Iinterview_11 |  -.0010307   .1668576    -0.01   0.995    -.3285599    .3264985
_Iinterview_12 |  -.0182252   .1692232    -0.11   0.914    -.3503978    .3139474
_Iinterview_13 |   -.003518   .1668253    -0.02   0.983    -.3309839    .3239478
_Iinterview_14 |   .0019918   .1677302     0.01   0.991    -.3272501    .3312338
_Iinterview_15 |   .0278293   .1239028     0.22   0.822    -.2153827    .2710413
_Iinterview_16 |   .0002335   .1211128     0.00   0.998     -.237502    .2379691
_Iinterview_17 |  -.0130869   .1199387    -0.11   0.913    -.2485177    .2223439
_Iinterview_18 |   .0095434   .1197269     0.08   0.936    -.2254717    .2445585
_Iinterview_19 |    .044727   .1199387     0.37   0.709    -.1907038    .2801579
_Iinterview_20 |  -.0083316   .1174893    -0.07   0.943    -.2389544    .2222912
_Iinterview_21 |  -.0371681   .0356399    -1.04   0.297    -.1071265    .0327904
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |  -.0403926   .0344412    -1.17   0.241    -.1079981    .0272128
_Iinterview_24 |  -.0404765    .033485    -1.21   0.227    -.1062051    .0252521
_Iinterview_25 |  -.0273705   .0840229    -0.33   0.745    -.1923012    .1375601
_Iinterview_26 |  -.0396148   .0451621    -0.88   0.381    -.1282646     .049035
_Iinterview_27 |  -.0540486   .0409285    -1.32   0.187    -.1343882     .026291
_Iinterview_28 |  -.0195285   .1988316    -0.10   0.922    -.4098204    .3707633
         _cons |   .0655036   .0373443     1.75   0.080    -.0078006    .1388078
--------------------------------------------------------------------------------
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_5 omitted because of collinearity
note: _Iinterview_15 omitted because of collinearity
note: _Iinterview_22 omitted because of collinearity
note: _Iinterview_28 omitted because of collinearity

Linear regression                                      Number of obs =     401
                                                       F( 51,   142) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.1743
                                                       Root MSE      =  .17957

                                     (Std. Err. adjusted for 143 clusters in ea)
--------------------------------------------------------------------------------
               |               Robust
      simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |   .0184827   .0327589     0.56   0.574    -.0462754    .0832408
       hotline |   .0171185   .0256592     0.67   0.506    -.0336048    .0678419
       verdade |  -.0267603   .0288754    -0.93   0.356    -.0838414    .0303209
           pr1 |    .116032   .0923569     1.26   0.211    -.0665402    .2986041
           pr2 |  -.0990006   .1208696    -0.82   0.414     -.337937    .1399358
           pr3 |  -.0336631   .1338703    -0.25   0.802    -.2982993    .2309731
          post |   .0009023   .0361478     0.02   0.980    -.0705551    .0723598
     post_miss |  -.0081486   .0249514    -0.33   0.744    -.0574727    .0411756
        health |   .0083303   .0207409     0.40   0.689    -.0326705    .0493311
   health_miss |  -.0443032   .0460448    -0.96   0.338    -.1353251    .0467187
        police |   .0120398   .0287388     0.42   0.676    -.0447715     .068851
   police_miss |   -.010023   .0695863    -0.14   0.886    -.1475819    .1275359
           sex |   .0586134   .0207431     2.83   0.005     .0176082    .0996186
           age |  -.0011202   .0008429    -1.33   0.186    -.0027864    .0005459
        single |  -.0217361   .0239494    -0.91   0.366    -.0690795    .0256072
         divor |   .0092636   .0388813     0.24   0.812    -.0675973    .0861245
       norelig |   .0947089   .0879233     1.08   0.283    -.0790989    .2685166
       protest |    -.01146   .0263112    -0.44   0.664    -.0634723    .0405522
           com |   .0038084   .0400432     0.10   0.924    -.0753494    .0829661
          prof |   .0681238   .1075438     0.63   0.527      -.14447    .2807176
       comform |  -.0178737    .029349    -0.61   0.543    -.0758911    .0401436
      econfood |    .014007   .0094544     1.48   0.141    -.0046825    .0326965
         house |  -.0121533   .0335079    -0.36   0.717    -.0783921    .0540855
          oven |   .0687148   .0475493     1.45   0.151    -.0252811    .1627108
        lchang |   .0294342     .06012     0.49   0.625    -.0894116      .14828
        llomue |  -.0016628   .0609594    -0.03   0.978    -.1221681    .1188424
       lchuabo |   .0027021   .0564092     0.05   0.962    -.1088082    .1142125
      lchitewe |   .3435161   .2064247     1.66   0.098    -.0645466    .7515788
        lronga |    .018498   .0457861     0.40   0.687    -.0720124    .1090084
       chitsua |  -.0180438   .0392598    -0.46   0.647     -.095653    .0595654
        living |   .0069779    .009019     0.77   0.440    -.0108509    .0248068
 _Iinterview_2 |  -.0486668   .0645978    -0.75   0.452    -.1763645    .0790309
 _Iinterview_3 |  -.0326108   .1288137    -0.25   0.801    -.2872512    .2220296
 _Iinterview_4 |  -.0143971    .132123    -0.11   0.913    -.2755793    .2467852
 _Iinterview_5 |          0  (omitted)
 _Iinterview_6 |  -.0348876    .121969    -0.29   0.775    -.2759973    .2062221
 _Iinterview_7 |   .0311201   .1261412     0.25   0.805    -.2182371    .2804774
 _Iinterview_8 |  -.0053423   .1331603    -0.04   0.968     -.268575    .2578905
 _Iinterview_9 |   .1395509   .1345235     1.04   0.301    -.1263766    .4054785
_Iinterview_10 |   .0067364   .0916539     0.07   0.942     -.174446    .1879188
_Iinterview_11 |   .0428564   .1156476     0.37   0.712    -.1857571    .2714699
_Iinterview_12 |   .0172616   .1048179     0.16   0.869    -.1899436    .2244667
_Iinterview_13 |   .0772005   .1091739     0.71   0.481    -.1386157    .2930166
_Iinterview_14 |   .0704405   .1288491     0.55   0.585    -.1842699    .3251508
_Iinterview_15 |          0  (omitted)
_Iinterview_16 |  -.1285496   .1149695    -1.12   0.265    -.3558225    .0987233
_Iinterview_17 |  -.1423279   .1039529    -1.37   0.173    -.3478231    .0631673
_Iinterview_18 |  -.1085451   .1032954    -1.05   0.295    -.3127406    .0956503
_Iinterview_19 |  -.0898483   .1096881    -0.82   0.414    -.3066809    .1269843
_Iinterview_20 |   .0586407   .1314532     0.45   0.656    -.2012175    .3184988
_Iinterview_21 |  -.0387379   .0579349    -0.67   0.505    -.1532642    .0757884
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |  -.0335828   .0552421    -0.61   0.544     -.142786    .0756205
_Iinterview_24 |  -.0213983   .0507944    -0.42   0.674    -.1218093    .0790127
_Iinterview_25 |  -.0321668   .0563304    -0.57   0.569    -.1435213    .0791877
_Iinterview_26 |  -.0335542   .0563113    -0.60   0.552     -.144871    .0777625
_Iinterview_27 |  -.0511551   .0544573    -0.94   0.349     -.158807    .0564968
_Iinterview_28 |          0  (omitted)
         _cons |   .0099925   .0554898     0.18   0.857    -.0997004    .1196853
--------------------------------------------------------------------------------
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_5 omitted because of collinearity
note: _Iinterview_15 omitted because of collinearity
note: _Iinterview_22 omitted because of collinearity
note: _Iinterview_28 omitted because of collinearity

      Source |       SS       df       MS              Number of obs =     401
-------------+------------------------------           F( 54,   346) =    1.35
       Model |  2.35481371    54  .043607661           Prob > F      =  0.0593
    Residual |  11.1564082   346  .032243954           R-squared     =  0.1743
-------------+------------------------------           Adj R-squared =  0.0454
       Total |  13.5112219   400  .033778055           Root MSE      =  .17957

--------------------------------------------------------------------------------
      simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |   .0184827   .0309082     0.60   0.550    -.0423089    .0792744
       hotline |   .0171185   .0291426     0.59   0.557    -.0402004    .0744374
       verdade |  -.0267603   .0321913    -0.83   0.406    -.0900755     .036555
           pr1 |    .116032   .0883249     1.31   0.190    -.0576893    .2897532
           pr2 |  -.0990006    .266322    -0.37   0.710    -.6228145    .4248133
           pr3 |  -.0336631   .3302782    -0.10   0.919    -.6832687    .6159426
          post |   .0009023   .0341436     0.03   0.979    -.0662529    .0680576
     post_miss |  -.0081486   .0500247    -0.16   0.871    -.1065394    .0902423
        health |   .0083303   .0248489     0.34   0.738    -.0405435    .0572041
   health_miss |  -.0443032   .0873455    -0.51   0.612    -.2160981    .1274917
        police |   .0120398   .0286988     0.42   0.675    -.0444063    .0684858
   police_miss |   -.010023   .1368809    -0.07   0.942    -.2792463    .2592003
           sex |   .0586134   .0199063     2.94   0.003     .0194608    .0977659
           age |  -.0011202   .0008301    -1.35   0.178    -.0027529    .0005125
        single |  -.0217361    .026375    -0.82   0.410    -.0736115    .0301393
         divor |   .0092636   .1105396     0.08   0.933    -.2081505    .2266777
       norelig |   .0947089   .0559672     1.69   0.092    -.0153699    .2047876
       protest |    -.01146   .0256603    -0.45   0.655    -.0619298    .0390097
           com |   .0038084   .0523283     0.07   0.942    -.0991132    .1067299
          prof |   .0681238   .0700868     0.97   0.332    -.0697261    .2059737
       comform |  -.0178737   .0785784    -0.23   0.820    -.1724252    .1366777
      econfood |    .014007   .0092546     1.51   0.131    -.0041954    .0322094
         house |  -.0121533   .0322994    -0.38   0.707    -.0756811    .0513745
          oven |   .0687148   .0409411     1.68   0.094    -.0118099    .1492395
        lchang |   .0294342    .049517     0.59   0.553    -.0679581    .1268265
        llomue |  -.0016628   .0444135    -0.04   0.970    -.0890172    .0856915
       lchuabo |   .0027021   .0414198     0.07   0.948    -.0787641    .0841683
      lchitewe |   .3435161    .088534     3.88   0.000     .1693835    .5176486
        lronga |    .018498    .038418     0.48   0.630    -.0570643    .0940603
       chitsua |  -.0180438   .1123584    -0.16   0.873    -.2390353    .2029477
        living |   .0069779   .0093438     0.75   0.456    -.0113999    .0253558
 _Iinterview_2 |  -.0486668   .1339618    -0.36   0.717    -.3121489    .2148152
 _Iinterview_3 |  -.0326108    .328524    -0.10   0.921    -.6787663    .6135446
 _Iinterview_4 |  -.0143971   .3273592    -0.04   0.965    -.6582616    .6294675
 _Iinterview_5 |          0  (omitted)
 _Iinterview_6 |  -.0348876   .3323725    -0.10   0.916    -.6886124    .6188373
 _Iinterview_7 |   .0311201   .3235455     0.10   0.923    -.6052433    .6674836
 _Iinterview_8 |  -.0053423   .3287669    -0.02   0.987    -.6519754    .6412909
 _Iinterview_9 |   .1395509   .2659912     0.52   0.600    -.3836122     .662714
_Iinterview_10 |   .0067364   .1869508     0.04   0.971    -.3609666    .3744394
_Iinterview_11 |   .0428564   .2679339     0.16   0.873    -.4841277    .5698405
_Iinterview_12 |   .0172616   .2679702     0.06   0.949     -.509794    .5443171
_Iinterview_13 |   .0772005   .2640724     0.29   0.770    -.4421887    .5965897
_Iinterview_14 |   .0704405   .2657271     0.27   0.791    -.4522034    .5930843
_Iinterview_15 |          0  (omitted)
_Iinterview_16 |  -.1285496   .0837329    -1.54   0.126    -.2932391      .03614
_Iinterview_17 |  -.1423279   .0837675    -1.70   0.090    -.3070855    .0224297
_Iinterview_18 |  -.1085451   .0812428    -1.34   0.182     -.268337    .0512468
_Iinterview_19 |  -.0898483   .0813291    -1.10   0.270    -.2498099    .0701134
_Iinterview_20 |   .0586407     .08472     0.69   0.489    -.1079904    .2252717
_Iinterview_21 |  -.0387379   .0592959    -0.65   0.514    -.1553637    .0778879
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |  -.0335828   .0589006    -0.57   0.569     -.149431    .0822654
_Iinterview_24 |  -.0213983   .0552617    -0.39   0.699    -.1300894    .0872928
_Iinterview_25 |  -.0321668   .1016152    -0.32   0.752     -.232028    .1676944
_Iinterview_26 |  -.0335542   .0854346    -0.39   0.695    -.2015908    .1344824
_Iinterview_27 |  -.0511551    .073475    -0.70   0.487    -.1956691    .0933588
_Iinterview_28 |          0  (omitted)
         _cons |   .0099925   .0615212     0.16   0.871    -.1110102    .1309951
--------------------------------------------------------------------------------

Simultaneous results for simango2_4_2a, simango2_4_3a

                                                  Number of obs   =       1014

                                          (Std. Err. adjusted for 161 clusters in ea)
-------------------------------------------------------------------------------------
                    |               Robust
                    |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
simango2_4_2a_mean  |
          civiceduc |   .0098386   .0160064     0.61   0.539    -.0215334    .0412106
            hotline |  -.0064653   .0134543    -0.48   0.631    -.0328353    .0199047
            verdade |  -.0204776   .0146776    -1.40   0.163    -.0492453      .00829
                pr1 |  -.0283149   .0287807    -0.98   0.325    -.0847241    .0280943
                pr2 |   .0141165   .0440196     0.32   0.748    -.0721603    .1003934
                pr3 |   .1078522   .0676685     1.59   0.111    -.0247756    .2404801
               post |  -.0013091    .024581    -0.05   0.958     -.049487    .0468688
          post_miss |   -.015527   .0150009    -1.04   0.301    -.0449283    .0138743
             health |  -.0068027   .0117615    -0.58   0.563    -.0298548    .0162494
        health_miss |  -.0440334    .021196    -2.08   0.038    -.0855768   -.0024901
             police |   .0067131   .0146943     0.46   0.648    -.0220871    .0355133
        police_miss |   .0355723    .031856     1.12   0.264    -.0268644     .098009
                sex |   .0340723   .0118695     2.87   0.004     .0108085     .057336
                age |  -.0010005   .0004259    -2.35   0.019    -.0018353   -.0001657
             single |  -.0161922   .0136031    -1.19   0.234    -.0428538    .0104695
              divor |  -.0230205   .0176678    -1.30   0.193    -.0576487    .0116077
            norelig |   .0096537   .0381939     0.25   0.800    -.0652049    .0845124
            protest |   -.003467   .0151306    -0.23   0.819    -.0331225    .0261884
                com |  -.0268219   .0100157    -2.68   0.007    -.0464524   -.0071914
               prof |   .0568195   .0769284     0.74   0.460    -.0939573    .2075964
            comform |  -.0285766    .017914    -1.60   0.111    -.0636873    .0065342
           econfood |   .0064567   .0050811     1.27   0.204    -.0035021    .0164155
              house |   .0017518   .0137854     0.13   0.899     -.025267    .0287706
               oven |   .0503783   .0263203     1.91   0.056    -.0012085    .1019651
             lchang |  -.0407543   .0380771    -1.07   0.284    -.1153841    .0338755
             llomue |  -.0137359   .0349186    -0.39   0.694     -.082175    .0547032
            lchuabo |   .0477175   .0300684     1.59   0.113    -.0112155    .1066505
           lchitewe |    .058547   .1448662     0.40   0.686    -.2253856    .3424796
             lronga |   .0103992   .0231404     0.45   0.653     -.034955    .0557535
            chitsua |  -.0264866   .0225137    -1.18   0.239    -.0706127    .0176394
             living |  -.0007178   .0052561    -0.14   0.891    -.0110196     .009584
      _Iinterview_2 |  -.0421208   .0425907    -0.99   0.323    -.1255971    .0413555
      _Iinterview_3 |  -.1078339   .0478433    -2.25   0.024     -.201605   -.0140628
      _Iinterview_4 |  -.0724998   .0463482    -1.56   0.118    -.1633406     .018341
      _Iinterview_5 |  -.0736292   .0431472    -1.71   0.088    -.1581961    .0109378
      _Iinterview_6 |  -.0976529   .0455115    -2.15   0.032    -.1868538   -.0084521
      _Iinterview_7 |  -.0230159   .0472984    -0.49   0.627    -.1157191    .0696872
      _Iinterview_8 |  -.1064918   .0475154    -2.24   0.025    -.1996202   -.0133634
      _Iinterview_9 |   .0334018   .0455193     0.73   0.463    -.0558145    .1226181
     _Iinterview_10 |  -.0248533   .0449206    -0.55   0.580     -.112896    .0631893
     _Iinterview_11 |  -.0010307   .0497989    -0.02   0.983    -.0986348    .0965734
     _Iinterview_12 |  -.0182252    .042802    -0.43   0.670    -.1021156    .0656652
     _Iinterview_13 |   -.003518   .0397804    -0.09   0.930    -.0814862    .0744501
     _Iinterview_14 |   .0019918   .0446088     0.04   0.964    -.0854397    .0894234
     _Iinterview_15 |   .0278293   .0656059     0.42   0.671     -.100756    .1564146
     _Iinterview_16 |   .0002335   .0528462     0.00   0.996    -.1033432    .1038102
     _Iinterview_17 |  -.0130869   .0399817    -0.33   0.743    -.0914495    .0652757
     _Iinterview_18 |   .0095434    .045585     0.21   0.834    -.0798016    .0988885
     _Iinterview_19 |    .044727   .0539139     0.83   0.407    -.0609423    .1503963
     _Iinterview_20 |  -.0083316   .0398998    -0.21   0.835    -.0865339    .0698706
     _Iinterview_21 |  -.0371681   .0318551    -1.17   0.243    -.0996029    .0252668
     _Iinterview_22 |          0  (omitted)
     _Iinterview_23 |  -.0403926    .030183    -1.34   0.181    -.0995503    .0187651
     _Iinterview_24 |  -.0404765   .0308646    -1.31   0.190    -.1009701    .0200171
     _Iinterview_25 |  -.0273705   .0349242    -0.78   0.433    -.0958206    .0410796
     _Iinterview_26 |  -.0396148   .0323075    -1.23   0.220    -.1029363    .0237067
     _Iinterview_27 |  -.0540486   .0328118    -1.65   0.100    -.1183585    .0102613
     _Iinterview_28 |  -.0195285   .0514075    -0.38   0.704    -.1202854    .0812284
              _cons |   .0655036   .0358534     1.83   0.068    -.0047678     .135775
--------------------+----------------------------------------------------------------
simango2_4_2a_lnvar |
              _cons |  -3.692474   .1764252   -20.93   0.000    -4.038261   -3.346687
--------------------+----------------------------------------------------------------
simango2_4_3a_mean  |
          civiceduc |   .0184827   .0304555     0.61   0.544     -.041209    .0781745
            hotline |   .0171185    .023855     0.72   0.473    -.0296365    .0638735
            verdade |  -.0267603   .0268451    -1.00   0.319    -.0793756    .0258551
                pr1 |    .116032   .0858631     1.35   0.177    -.0522566    .2843205
                pr2 |  -.0990006    .112371    -0.88   0.378    -.3192437    .1212425
                pr3 |  -.0336631   .1244575    -0.27   0.787    -.2775954    .2102692
               post |   .0009023   .0336062     0.03   0.979    -.0649646    .0667693
          post_miss |  -.0081486    .023197    -0.35   0.725    -.0536138    .0373167
             health |   .0083303   .0192825     0.43   0.666    -.0294628    .0461234
        health_miss |  -.0443032   .0428073    -1.03   0.301    -.1282039    .0395976
             police |   .0120398   .0267181     0.45   0.652    -.0403268    .0644064
        police_miss |   -.010023   .0646935    -0.15   0.877      -.13682    .1167739
                sex |   .0586134   .0192846     3.04   0.002     .0208162    .0964105
                age |  -.0011202   .0007836    -1.43   0.153     -.002656    .0004156
             single |  -.0217361   .0222654    -0.98   0.329    -.0653755    .0219033
              divor |   .0092636   .0361474     0.26   0.798    -.0615841    .0801112
            norelig |   .0947089   .0817412     1.16   0.247     -.065501    .2549187
            protest |    -.01146   .0244612    -0.47   0.639    -.0594031     .036483
                com |   .0038084   .0372277     0.10   0.919    -.0691565    .0767732
               prof |   .0681238   .0999822     0.68   0.496    -.1278377    .2640853
            comform |  -.0178737   .0272854    -0.66   0.512    -.0713521    .0356046
           econfood |    .014007   .0087896     1.59   0.111    -.0032204    .0312343
              house |  -.0121533   .0311519    -0.39   0.696    -.0732099    .0489033
               oven |   .0687148    .044206     1.55   0.120    -.0179273     .155357
             lchang |   .0294342   .0558928     0.53   0.598    -.0801136    .1389821
             llomue |  -.0016628   .0566732    -0.03   0.977    -.1127403    .1094147
            lchuabo |   .0027021    .052443     0.05   0.959    -.1000842    .1054884
           lchitewe |   .3435161   .1919106     1.79   0.073    -.0326217    .7196539
             lronga |    .018498   .0425668     0.43   0.664    -.0649313    .1019273
            chitsua |  -.0180438   .0364994    -0.49   0.621    -.0895813    .0534936
             living |   .0069779   .0083849     0.83   0.405    -.0094561     .023412
      _Iinterview_2 |  -.0486668   .0600558    -0.81   0.418    -.1663741    .0690404
      _Iinterview_3 |  -.0326108   .1197566    -0.27   0.785    -.2673294    .2021077
      _Iinterview_4 |  -.0143971   .1228332    -0.12   0.907    -.2551456    .2263515
      _Iinterview_5 |          0  (omitted)
      _Iinterview_6 |  -.0348876   .1133931    -0.31   0.758     -.257134    .1873588
      _Iinterview_7 |   .0311201   .1172719     0.27   0.791    -.1987286    .2609689
      _Iinterview_8 |  -.0053423   .1237975    -0.04   0.966    -.2479809    .2372964
      _Iinterview_9 |   .1395509   .1250649     1.12   0.264    -.1055717    .3846736
     _Iinterview_10 |   .0067364   .0852095     0.08   0.937    -.1602712     .173744
     _Iinterview_11 |   .0428564   .1075162     0.40   0.690    -.1678714    .2535843
     _Iinterview_12 |   .0172616   .0974479     0.18   0.859    -.1737329     .208256
     _Iinterview_13 |   .0772005   .1014976     0.76   0.447    -.1217312    .2761322
     _Iinterview_14 |   .0704405   .1197895     0.59   0.557    -.1643426    .3052235
     _Iinterview_15 |          0  (omitted)
     _Iinterview_16 |  -.1285496   .1068857    -1.20   0.229    -.3380417    .0809426
     _Iinterview_17 |  -.1423279   .0966438    -1.47   0.141    -.3317462    .0470904
     _Iinterview_18 |  -.1085451   .0960325    -1.13   0.258    -.2967653    .0796751
     _Iinterview_19 |  -.0898483   .1019757    -0.88   0.378    -.2897169    .1100203
     _Iinterview_20 |   .0586407   .1222105     0.48   0.631    -.1808874    .2981688
     _Iinterview_21 |  -.0387379   .0538614    -0.72   0.472    -.1443042    .0668284
     _Iinterview_22 |          0  (omitted)
     _Iinterview_23 |  -.0335828   .0513579    -0.65   0.513    -.1342425    .0670769
     _Iinterview_24 |  -.0213983    .047223    -0.45   0.650    -.1139536     .071157
     _Iinterview_25 |  -.0321668   .0523697    -0.61   0.539    -.1348094    .0704758
     _Iinterview_26 |  -.0335542   .0523519    -0.64   0.522    -.1361621    .0690536
     _Iinterview_27 |  -.0511551   .0506283    -1.01   0.312    -.1503848    .0480746
     _Iinterview_28 |          0  (omitted)
              _cons |   .0099925   .0515882     0.19   0.846    -.0911186    .1111035
--------------------+----------------------------------------------------------------
simango2_4_3a_lnvar |
              _cons |  -3.434425   .1997959   -17.19   0.000    -3.826017   -3.042832
-------------------------------------------------------------------------------------

 ( 1)  [simango2_4_2a_mean]civiceduc - [simango2_4_3a_mean]civiceduc = 0

           chi2(  1) =    0.08
         Prob > chi2 =    0.7710
.77103797

 ( 1)  [simango2_4_2a_mean]hotline - [simango2_4_3a_mean]hotline = 0

           chi2(  1) =    0.87
         Prob > chi2 =    0.3512
.35117936

 ( 1)  [simango2_4_2a_mean]verdade - [simango2_4_3a_mean]verdade = 0

           chi2(  1) =    0.05
         Prob > chi2 =    0.8198
.81982513

. 
. matrix define means=(m_simango2_2_1, m_simango2_3_1, m_simango2_4_1 \ t_simango2_2_1_1, t_sima
> ngo2_3_1_1, t_simango2_4_1_1 \ t_simango2_2_1_2, t_simango2_3_1_2, t_simango2_4_1_2 \ t_simang
> o2_2_1_3, t_simango2_3_1_3, t_simango2_4_1_3 \ t_simango2_2_1_4, t_simango2_3_1_4, t_simango2_
> 4_1_4 \ t_simango2_2_5, t_simango2_3_5, t_simango2_4_5 \ t_simango2_2_6, t_simango2_3_6, t_sim
> ango2_4_6 \ t_simango2_2_7, t_simango2_3_7, t_simango2_4_7)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_voting.xml") append sheet("voting
>  3") 


note: results saved to outputregs_voting.xml

. xml_tab $list2, save("outputregs_voting.xml") append sheet("voting 3 stats") 


note: results saved to outputregs_voting.xml

. estimates clear

. 
. global list1=""

. global list2=""

. 
. foreach i in $voting4 {
  2. 
.         regress `i' $treat $prov if time==1, cluster(ea)
  3.         estimates store `i'_2_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2_1=r(mean)
  6.         display m_`i'_2_1
  7.         test civiceduc = hotline
  8.         scalar define t_`i'_2_1_1=r(p)
  9.         display t_`i'_2_1_1
 10.         test civiceduc = verdade
 11.         scalar define t_`i'_2_1_2=r(p)
 12.         display t_`i'_2_1_2
 13.         test hotline = verdade
 14.         scalar define t_`i'_2_1_3=r(p)
 15.         display t_`i'_2_1_3
 16.         test civiceduc hotline verdade
 17.         scalar define t_`i'_2_1_4=r(p)
 18.         display t_`i'_2_1_4
 19. 
.         regress `i' $treat $prov if time==1 & lazy==0, cluster(ea)
 20.         estimates store `i'_2_2
 21.         regress `i' $treat $prov if time==1 & lazy==0
 22.         estimates store `i'_2_2a
 23.         
.         regress `i' $treat $prov if time==1 & (lazy==1|control==1), cluster(ea)
 24.         estimates store `i'_2_3
 25.         regress `i' $treat $prov if time==1 & (lazy==1|control==1)
 26.         estimates store `i'_2_3a
 27. 
.         suest `i'_2_2a `i'_2_3a, cluster(ea)
 28.         test [`i'_2_2a_mean]civiceduc=[`i'_2_3a_mean]civiceduc  
 29.         scalar define t_`i'_2_5=r(p)
 30.         display t_`i'_2_5
 31.         test [`i'_2_2a_mean]hotline=[`i'_2_3a_mean]hotline      
 32.         scalar define t_`i'_2_6=r(p)
 33.         display t_`i'_2_6
 34.         test [`i'_2_2a_mean]verdade=[`i'_2_3a_mean]verdade
 35.         scalar define t_`i'_2_7=r(p)
 36.         display t_`i'_2_7
 37. 
.         regress `i' $treat $prov $ea $controls if time==1, cluster(ea)
 38.         estimates store `i'_3_1
 39.         sum `i' if e(sample) & control == 1
 40.         scalar define m_`i'_3_1=r(mean)
 41.         display m_`i'_3_1
 42.         test civiceduc = hotline
 43.         scalar define t_`i'_3_1_1=r(p)
 44.         display t_`i'_3_1_1
 45.         test civiceduc = verdade
 46.         scalar define t_`i'_3_1_2=r(p)
 47.         display t_`i'_3_1_2
 48.         test hotline = verdade
 49.         scalar define t_`i'_3_1_3=r(p)
 50.         display t_`i'_3_1_3
 51.         test civiceduc hotline verdade
 52.         scalar define t_`i'_3_1_4=r(p)
 53.         display t_`i'_3_1_4
 54. 
.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0, cluster(ea)
 55.         estimates store `i'_3_2
 56.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0
 57.         estimates store `i'_3_2a
 58.         
.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1), cluster(ea)
 59.         estimates store `i'_3_3
 60.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1)
 61.         estimates store `i'_3_3a
 62. 
.         suest `i'_3_2a `i'_3_3a, cluster(ea)
 63.         test [`i'_3_2a_mean]civiceduc=[`i'_3_3a_mean]civiceduc  
 64.         scalar define t_`i'_3_5=r(p)
 65.         display t_`i'_3_5
 66.         test [`i'_3_2a_mean]hotline=[`i'_3_3a_mean]hotline      
 67.         scalar define t_`i'_3_6=r(p)
 68.         display t_`i'_3_6
 69.         test [`i'_3_2a_mean]verdade=[`i'_3_3a_mean]verdade
 70.         scalar define t_`i'_3_7=r(p)
 71.         display t_`i'_3_7
 72. 
.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1, cluster(ea)
 73.         estimates store `i'_4_1
 74.         sum `i' if e(sample) & control == 1
 75.         scalar define m_`i'_4_1=r(mean)
 76.         display m_`i'_4_1
 77.         test civiceduc = hotline
 78.         scalar define t_`i'_4_1_1=r(p)
 79.         display t_`i'_4_1_1
 80.         test civiceduc = verdade
 81.         scalar define t_`i'_4_1_2=r(p)
 82.         display t_`i'_4_1_2
 83.         test hotline = verdade
 84.         scalar define t_`i'_4_1_3=r(p)
 85.         display t_`i'_4_1_3
 86.         test civiceduc hotline verdade
 87.         scalar define t_`i'_4_1_4=r(p)
 88.         display t_`i'_4_1_4
 89. 
.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & lazy==0, cluster
> (ea)
 90.         estimates store `i'_4_2
 91.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & lazy==0
 92.         estimates store `i'_4_2a
 93.         
.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & (lazy==1|control
> ==1), cluster(ea)
 94.         estimates store `i'_4_3
 95.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & (lazy==1|cont
> rol==1)
 96.         estimates store `i'_4_3a
 97. 
.         suest `i'_4_2a `i'_4_3a, cluster(ea)
 98.         test [`i'_4_2a_mean]civiceduc=[`i'_4_3a_mean]civiceduc  
 99.         scalar define t_`i'_4_5=r(p)
100.         display t_`i'_4_5
101.         test [`i'_4_2a_mean]hotline=[`i'_4_3a_mean]hotline      
102.         scalar define t_`i'_4_6=r(p)
103.         display t_`i'_4_6
104.         test [`i'_4_2a_mean]verdade=[`i'_4_3a_mean]verdade
105.         scalar define t_`i'_4_7=r(p)
106.         display t_`i'_4_7
107.         
.         global list1="$list1" + " `i'_2_1" + " `i'_3_1" + " `i'_4_1" + " `i'_2_2" + " `i'_3_2"
>  + " `i'_4_2"  + " `i'_2_3" + " `i'_3_3" + " `i'_4_3"
108.                 
.         }

Linear regression                                      Number of obs =    1048
                                                       F(  6,   160) =    4.28
                                                       Prob > F      =  0.0005
                                                       R-squared     =  0.0360
                                                       Root MSE      =  .34879

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .042525   .0334256     1.27   0.205    -.0234873    .1085373
     hotline |   .0607715   .0309706     1.96   0.051    -.0003924    .1219354
     verdade |   .0155071   .0375831     0.41   0.680    -.0587159    .0897301
         pr1 |  -.1476434   .0381782    -3.87   0.000    -.2230417   -.0722452
         pr2 |  -.0771505   .0253634    -3.04   0.003    -.1272407   -.0270602
         pr3 |  -.0017009   .0235178    -0.07   0.942    -.0481462    .0447444
       _cons |   .8791847   .0272955    32.21   0.000     .8252788    .9330906
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    frelimo2 |       252    .8214286    .3837552          0          1
.82142857

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.41
            Prob > F =    0.5226
.52256145

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.58
            Prob > F =    0.4465
.44645627

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    1.91
            Prob > F =    0.1694
.16937881

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.54
            Prob > F =    0.2064
.20642966

Linear regression                                      Number of obs =     886
                                                       F(  6,   160) =    4.85
                                                       Prob > F      =  0.0001
                                                       R-squared     =  0.0399
                                                       Root MSE      =  .35028

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0443447   .0331561     1.34   0.183    -.0211353    .1098247
     hotline |   .0739408   .0304659     2.43   0.016     .0137736     .134108
     verdade |   .0037356   .0402047     0.09   0.926    -.0756648    .0831359
         pr1 |  -.1540021    .038111    -4.04   0.000    -.2292675   -.0787367
         pr2 |  -.0960489   .0271279    -3.54   0.001    -.1496238    -.042474
         pr3 |  -.0157476     .02433    -0.65   0.518     -.063797    .0323018
       _cons |   .8889231   .0269159    33.03   0.000     .8357668    .9420794
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     886
-------------+------------------------------           F(  6,   879) =    6.10
       Model |  4.48728677     6  .747881128           Prob > F      =  0.0000
    Residual |  107.846799   879  .122692604           R-squared     =  0.0399
-------------+------------------------------           Adj R-squared =  0.0334
       Total |  112.334086   885   .12693117           Root MSE      =  .35028

------------------------------------------------------------------------------
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0443447   .0321659     1.38   0.168    -.0187862    .1074756
     hotline |   .0739408   .0327579     2.26   0.024     .0096479    .1382336
     verdade |   .0037356   .0332084     0.11   0.910    -.0614415    .0689126
         pr1 |  -.1540021   .0327865    -4.70   0.000     -.218351   -.0896531
         pr2 |  -.0960489   .0336849    -2.85   0.004    -.1621611   -.0299367
         pr3 |  -.0157476   .0333817    -0.47   0.637    -.0812648    .0497696
       _cons |   .8889231   .0301584    29.48   0.000     .8297322    .9481139
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     414
                                                       F(  6,   145) =    1.95
                                                       Prob > F      =  0.0759
                                                       R-squared     =  0.0205
                                                       Root MSE      =  .36762

                                   (Std. Err. adjusted for 146 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0372012   .0617435     0.60   0.548    -.0848323    .1592348
     hotline |   .0156559   .0491713     0.32   0.751    -.0815291     .112841
     verdade |   .0645398   .0471301     1.37   0.173    -.0286109    .1576904
         pr1 |  -.0501851   .0550983    -0.91   0.364    -.1590846    .0587144
         pr2 |  -.0815776    .047063    -1.73   0.085    -.1745957    .0114404
         pr3 |   .0439207   .0389061     1.13   0.261    -.0329757    .1208171
       _cons |   .8425422   .0310398    27.14   0.000     .7811932    .9038912
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     414
-------------+------------------------------           F(  6,   407) =    1.42
       Model |  1.15183088     6  .191971813           Prob > F      =  0.2053
    Residual |   55.005174   407  .135147848           R-squared     =  0.0205
-------------+------------------------------           Adj R-squared =  0.0061
       Total |  56.1570048   413  .135973377           Root MSE      =  .36762

------------------------------------------------------------------------------
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0372012   .0560707     0.66   0.507     -.073023    .1474255
     hotline |   .0156559   .0533246     0.29   0.769    -.0891701    .1204819
     verdade |   .0645398   .0565099     1.14   0.254     -.046548    .1756275
         pr1 |  -.0501851   .0509424    -0.99   0.325    -.1503282     .049958
         pr2 |  -.0815776   .0521755    -1.56   0.119    -.1841447    .0209894
         pr3 |   .0439207   .0499612     0.88   0.380    -.0542936     .142135
       _cons |   .8425422   .0390271    21.59   0.000     .7658224    .9192621
------------------------------------------------------------------------------

Simultaneous results for frelimo2_2_2a, frelimo2_2_3a

                                                  Number of obs   =       1048

                                          (Std. Err. adjusted for 161 clusters in ea)
-------------------------------------------------------------------------------------
                    |               Robust
                    |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
frelimo2_2_2a_mean  |
          civiceduc |   .0443447   .0330435     1.34   0.180    -.0204194    .1091087
            hotline |   .0739408   .0303625     2.44   0.015     .0144314    .1334501
            verdade |   .0037356   .0400682     0.09   0.926    -.0747966    .0822677
                pr1 |  -.1540021   .0379816    -4.05   0.000    -.2284445   -.0795596
                pr2 |  -.0960489   .0270358    -3.55   0.000     -.149038   -.0430598
                pr3 |  -.0157476   .0242474    -0.65   0.516    -.0632716    .0317764
              _cons |   .8889231   .0268245    33.14   0.000      .836348    .9414982
--------------------+----------------------------------------------------------------
frelimo2_2_2a_lnvar |
              _cons |  -2.098073     .06983   -30.05   0.000    -2.234937   -1.961209
--------------------+----------------------------------------------------------------
frelimo2_2_3a_mean  |
          civiceduc |   .0372012   .0612737     0.61   0.544     -.082893    .1572955
            hotline |   .0156559   .0487971     0.32   0.748    -.0799847    .1112965
            verdade |   .0645398   .0467714     1.38   0.168    -.0271306    .1562101
                pr1 |  -.0501851    .054679    -0.92   0.359     -.157354    .0569838
                pr2 |  -.0815776   .0467049    -1.75   0.081    -.1731175    .0099622
                pr3 |   .0439207   .0386101     1.14   0.255    -.0317537     .119595
              _cons |   .8425422   .0308036    27.35   0.000     .7821682    .9029163
--------------------+----------------------------------------------------------------
frelimo2_2_3a_lnvar |
              _cons |  -2.001386   .0871605   -22.96   0.000    -2.172217   -1.830554
-------------------------------------------------------------------------------------

 ( 1)  [frelimo2_2_2a_mean]civiceduc - [frelimo2_2_3a_mean]civiceduc = 0

           chi2(  1) =    0.02
         Prob > chi2 =    0.9025
.90249884

 ( 1)  [frelimo2_2_2a_mean]hotline - [frelimo2_2_3a_mean]hotline = 0

           chi2(  1) =    1.72
         Prob > chi2 =    0.1900
.19000207

 ( 1)  [frelimo2_2_2a_mean]verdade - [frelimo2_2_3a_mean]verdade = 0

           chi2(  1) =    1.71
         Prob > chi2 =    0.1910
.19097654

Linear regression                                      Number of obs =    1034
                                                       F( 31,   160) =    2.09
                                                       Prob > F      =  0.0017
                                                       R-squared     =  0.0655
                                                       Root MSE      =  .34583

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0587762   .0326537     1.80   0.074    -.0057117    .1232641
     hotline |   .0704079   .0299305     2.35   0.020     .0112982    .1295176
     verdade |   .0322751   .0368237     0.88   0.382     -.040448    .1049982
         pr1 |    -.12713   .0615758    -2.06   0.041    -.2487362   -.0055238
         pr2 |  -.0718484   .0720094    -1.00   0.320      -.21406    .0703631
         pr3 |  -.0021754   .0738208    -0.03   0.977    -.1479642    .1436135
        post |   .0358428   .0457311     0.78   0.434    -.0544716    .1261571
   post_miss |    .028006   .0462771     0.61   0.546    -.0633867    .1193988
      health |   .0051591     .02218     0.23   0.816    -.0386442    .0489623
 health_miss |   .1068493   .0670901     1.59   0.113    -.0256472    .2393457
      police |  -.0305204   .0370483    -0.82   0.411     -.103687    .0426463
 police_miss |  -.2417045   .1352773    -1.79   0.076    -.5088638    .0254549
         sex |    .017918   .0210683     0.85   0.396    -.0236899    .0595259
         age |  -.0008259   .0010642    -0.78   0.439    -.0029277    .0012758
      single |  -.0490195   .0302591    -1.62   0.107    -.1087783    .0107393
       divor |  -.0851165   .1457219    -0.58   0.560     -.372903      .20267
     norelig |  -.0612539   .0608555    -1.01   0.316    -.1814375    .0589297
     protest |  -.0223039   .0314959    -0.71   0.480    -.0845052    .0398975
         com |  -.0371877   .0535838    -0.69   0.489    -.1430104    .0686349
        prof |   .0663637   .0633464     1.05   0.296    -.0587392    .1914666
     comform |  -.1483703   .1160215    -1.28   0.203    -.3775013    .0807607
    econfood |  -.0153185   .0104186    -1.47   0.143    -.0358942    .0052571
       house |   .0478127   .0368478     1.30   0.196     -.024958    .1205834
        oven |   .0507458   .0426027     1.19   0.235    -.0333903     .134882
      lchang |    -.01173   .0697567    -0.17   0.867    -.1494926    .1260326
      llomue |  -.0060309   .0638604    -0.09   0.925    -.1321489    .1200871
     lchuabo |  -.0082066   .0588013    -0.14   0.889    -.1243334    .1079202
    lchitewe |  -.0395988   .1371339    -0.29   0.773    -.3104248    .2312272
      lronga |  -.0256411   .0446849    -0.57   0.567    -.1138894    .0626072
     chitsua |   .0832556   .0822087     1.01   0.313    -.0790985    .2456097
      living |   .0288529   .0121957     2.37   0.019     .0047676    .0529382
       _cons |   .7948107   .0585121    13.58   0.000     .6792551    .9103664
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    frelimo2 |       250        .824    .3815841          0          1
.824

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.17
            Prob > F =    0.6793
.6793063

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.58
            Prob > F =    0.4475
.44746539

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    1.38
            Prob > F =    0.2425
.2425364

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    2.05
            Prob > F =    0.1086
.10856297

Linear regression                                      Number of obs =     876
                                                       F( 31,   160) =    2.71
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0736
                                                       Root MSE      =  .34747

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0600074   .0310273     1.93   0.055    -.0012685    .1212832
     hotline |   .0749102   .0305715     2.45   0.015     .0145345    .1352859
     verdade |   .0175025   .0398795     0.44   0.661    -.0612555    .0962606
         pr1 |   -.141201   .0642238    -2.20   0.029    -.2680367   -.0143652
         pr2 |  -.1355867   .0782544    -1.73   0.085    -.2901314    .0189581
         pr3 |  -.0697869   .0819244    -0.85   0.396    -.2315795    .0920057
        post |   .0342688   .0529718     0.65   0.519    -.0703453    .1388829
   post_miss |   .0171044   .0455299     0.38   0.708    -.0728126    .1070215
      health |   .0133716   .0232903     0.57   0.567    -.0326245    .0593676
 health_miss |   .0794806   .0756696     1.05   0.295    -.0699594    .2289206
      police |  -.0341908   .0423874    -0.81   0.421    -.1179019    .0495202
 police_miss |   -.180154   .1391242    -1.29   0.197    -.4549107    .0946027
         sex |   .0372544   .0242881     1.53   0.127    -.0107122     .085221
         age |  -.0010033   .0011381    -0.88   0.379     -.003251    .0012443
      single |   -.058703   .0333119    -1.76   0.080    -.1244907    .0070847
       divor |   .0277867   .1313014     0.21   0.833    -.2315206     .287094
     norelig |  -.0335827   .0631895    -0.53   0.596    -.1583757    .0912102
     protest |  -.0087096   .0355541    -0.24   0.807    -.0789255    .0615062
         com |  -.0096898   .0549281    -0.18   0.860    -.1181674    .0987877
        prof |   .1317417   .0314303     4.19   0.000     .0696699    .1938135
     comform |   -.039588   .1084026    -0.37   0.715    -.2536724    .1744964
    econfood |   -.013084   .0112688    -1.16   0.247    -.0353388    .0091708
       house |   .0532627   .0417446     1.28   0.204    -.0291787    .1357042
        oven |   .0656494   .0433372     1.51   0.132    -.0199374    .1512362
      lchang |   .0329053   .0780561     0.42   0.674    -.1212479    .1870584
      llomue |    .010255   .0659911     0.16   0.877     -.120071    .1405811
     lchuabo |   -.004288   .0598761    -0.07   0.943    -.1225374    .1139613
    lchitewe |   .0544075   .1473165     0.37   0.712     -.236528    .3453431
      lronga |  -.0359406   .0498669    -0.72   0.472    -.1344228    .0625415
     chitsua |   .0808465   .0988039     0.82   0.414    -.1142814    .2759744
      living |    .036784   .0127761     2.88   0.005     .0115524    .0620156
       _cons |   .7615473    .062269    12.23   0.000     .6385721    .8845226
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     876
-------------+------------------------------           F( 31,   844) =    2.16
       Model |  8.09997161    31  .261289407           Prob > F      =  0.0003
    Residual |  101.903453   844  .120738688           R-squared     =  0.0736
-------------+------------------------------           Adj R-squared =  0.0396
       Total |  110.003425   875    .1257182           Root MSE      =  .34747

------------------------------------------------------------------------------
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0600074   .0342026     1.75   0.080    -.0071247    .1271394
     hotline |   .0749102   .0338762     2.21   0.027     .0084188    .1414016
     verdade |   .0175025   .0352132     0.50   0.619    -.0516132    .0866183
         pr1 |   -.141201    .052936    -2.67   0.008    -.2451027   -.0372993
         pr2 |  -.1355867   .0704179    -1.93   0.055    -.2738015    .0026281
         pr3 |  -.0697869   .0725366    -0.96   0.336    -.2121602    .0725864
        post |   .0342688   .0488335     0.70   0.483    -.0615805     .130118
   post_miss |   .0171044   .0788534     0.22   0.828    -.1376673    .1718762
      health |   .0133716   .0291919     0.46   0.647    -.0439257    .0706688
 health_miss |   .0794806    .095344     0.83   0.405    -.1076585    .2666197
      police |  -.0341908   .0361174    -0.95   0.344    -.1050812    .0366996
 police_miss |   -.180154    .148703    -1.21   0.226    -.4720251    .1117171
         sex |   .0372544   .0253663     1.47   0.142     -.012534    .0870428
         age |  -.0010033   .0009843    -1.02   0.308    -.0029353    .0009286
      single |   -.058703   .0331445    -1.77   0.077    -.1237583    .0063523
       divor |   .0277867   .1344956     0.21   0.836    -.2361984    .2917719
     norelig |  -.0335827   .0622365    -0.54   0.590    -.1557392    .0885737
     protest |  -.0087096   .0299789    -0.29   0.771    -.0675516    .0501324
         com |  -.0096898   .0561349    -0.17   0.863    -.1198702    .1004905
        prof |   .1317417   .0994948     1.32   0.186    -.0635445    .3270279
     comform |   -.039588   .1130832    -0.35   0.726    -.2615453    .1823693
    econfood |   -.013084   .0105719    -1.24   0.216    -.0338342    .0076662
       house |   .0532627   .0355017     1.50   0.134    -.0164192    .1229447
        oven |   .0656494   .0465508     1.41   0.159    -.0257196    .1570184
      lchang |   .0329053   .0623216     0.53   0.598    -.0894183    .1552289
      llomue |    .010255   .0516228     0.20   0.843     -.091069    .1115791
     lchuabo |   -.004288   .0488412    -0.09   0.930    -.1001526    .0915765
    lchitewe |   .0544075   .1361014     0.40   0.689    -.2127293    .3215444
      lronga |  -.0359406   .0500526    -0.72   0.473    -.1341828    .0623015
     chitsua |   .0808465   .1154321     0.70   0.484    -.1457212    .3074142
      living |    .036784    .011885     3.09   0.002     .0134563    .0601116
       _cons |   .7615473   .0642406    11.85   0.000     .6354572    .8876375
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     408
                                                       F( 31,   143) =    5.99
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1006
                                                       Root MSE      =  .36153

                                   (Std. Err. adjusted for 144 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0443121   .0696624     0.64   0.526     -.093389    .1820132
     hotline |   .0494173   .0426303     1.16   0.248    -.0348497    .1336843
     verdade |   .1020275   .0468854     2.18   0.031     .0093495    .1947055
         pr1 |   .1222566   .0698096     1.75   0.082    -.0157354    .2602487
         pr2 |   .0062049   .1078274     0.06   0.954    -.2069367    .2193464
         pr3 |   .1541348     .11117     1.39   0.168    -.0656141    .3738837
        post |   .0434938   .0578201     0.75   0.453    -.0707986    .1577863
   post_miss |   .0012099   .0403525     0.03   0.976    -.0785546    .0809744
      health |  -.0235898   .0392444    -0.60   0.549     -.101164    .0539844
 health_miss |   .2374883   .0608959     3.90   0.000     .1171158    .3578607
      police |   .0244538   .0490094     0.50   0.619    -.0724227    .1213303
 police_miss |  -.2648234   .1604639    -1.65   0.101     -.582011    .0523643
         sex |  -.0292041   .0389412    -0.75   0.455    -.1061789    .0477706
         age |  -.0013772   .0022281    -0.62   0.537    -.0057816    .0030271
      single |  -.0613881   .0515963    -1.19   0.236    -.1633782     .040602
       divor |  -.2688653   .2700704    -1.00   0.321    -.8027114    .2649808
     norelig |  -.0884809   .1240339    -0.71   0.477    -.3336577    .1566959
     protest |  -.0436892   .0598242    -0.73   0.466    -.1619433    .0745649
         com |  -.1004059     .12479    -0.80   0.422    -.3470773    .1462656
        prof |   .0359865   .1028346     0.35   0.727    -.1672858    .2392588
     comform |  -.4905011   .2306985    -2.13   0.035     -.946521   -.0344812
    econfood |  -.0053344   .0121018    -0.44   0.660    -.0292558    .0185871
       house |  -.0038603   .0591392    -0.07   0.948    -.1207602    .1130396
        oven |  -.0251993   .0831404    -0.30   0.762    -.1895424    .1391437
      lchang |  -.1207329   .1011312    -1.19   0.235    -.3206382    .0791725
      llomue |  -.1536571   .0706861    -2.17   0.031    -.2933817   -.0139324
     lchuabo |  -.1762742   .0789066    -2.23   0.027    -.3322484   -.0203001
    lchitewe |  -.2878614   .2089275    -1.38   0.170    -.7008467    .1251239
      lronga |   .0368524   .0799029     0.46   0.645     -.121091    .1947959
     chitsua |   .1977512   .0793463     2.49   0.014     .0409081    .3545944
      living |     .02778    .021029     1.32   0.189    -.0137878    .0693478
       _cons |   .8666268   .1057565     8.19   0.000     .6575788    1.075675
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     408
-------------+------------------------------           F( 31,   376) =    1.36
       Model |  5.49993523    31  .177417266           Prob > F      =  0.1004
    Residual |  49.1446726   376  .130703917           R-squared     =  0.1006
-------------+------------------------------           Adj R-squared =  0.0265
       Total |  54.6446078   407  .134261936           Root MSE      =  .36153

------------------------------------------------------------------------------
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0443121   .0601161     0.74   0.462    -.0738937    .1625179
     hotline |   .0494173    .055926     0.88   0.377    -.0605497    .1593843
     verdade |   .1020275   .0606642     1.68   0.093     -.017256     .221311
         pr1 |   .1222566   .0875204     1.40   0.163    -.0498342    .2943475
         pr2 |   .0062049   .1097354     0.06   0.955    -.2095671    .2219769
         pr3 |   .1541348   .1123177     1.37   0.171    -.0667146    .3749842
        post |   .0434938   .0668793     0.65   0.516    -.0880106    .1749982
   post_miss |   .0012099   .0955804     0.01   0.990    -.1867292     .189149
      health |  -.0235898   .0476231    -0.50   0.621    -.1172308    .0700511
 health_miss |   .2374883    .173236     1.37   0.171    -.1031445     .578121
      police |   .0244538   .0561559     0.44   0.663    -.0859652    .1348728
 police_miss |  -.2648234   .2581201    -1.03   0.306    -.7723631    .2427164
         sex |  -.0292041   .0388719    -0.75   0.453    -.1056376    .0472293
         age |  -.0013772   .0016088    -0.86   0.393    -.0045406    .0017861
      single |  -.0613881   .0488899    -1.26   0.210      -.15752    .0347438
       divor |  -.2688653   .2139089    -1.26   0.210    -.6894729    .1517423
     norelig |  -.0884809   .1113207    -0.79   0.427    -.3073701    .1304083
     protest |  -.0436892   .0497661    -0.88   0.381    -.1415439    .0541655
         com |  -.1004059    .102469    -0.98   0.328      -.30189    .1010783
        prof |   .0359865    .135862     0.26   0.791     -.231158    .3031309
     comform |  -.4905011   .1522067    -3.22   0.001    -.7897842    -.191218
    econfood |  -.0053344   .0170611    -0.31   0.755    -.0388816    .0282128
       house |  -.0038603   .0545034    -0.07   0.944      -.11103    .1033094
        oven |  -.0251993   .0750793    -0.34   0.737    -.1728273    .1224287
      lchang |  -.1207329   .0967416    -1.25   0.213    -.3109553    .0694895
      llomue |  -.1536571   .0864764    -1.78   0.076     -.323695    .0163809
     lchuabo |  -.1762742   .0816617    -2.16   0.032    -.3368451   -.0157034
    lchitewe |  -.2878614     .17512    -1.64   0.101    -.6321987    .0564759
      lronga |   .0368524    .073831     0.50   0.618    -.1083209    .1820258
     chitsua |   .1977512   .1914242     1.03   0.302     -.178645    .5741474
      living |     .02778   .0180183     1.54   0.124    -.0076492    .0632092
       _cons |   .8666268   .0959091     9.04   0.000     .6780415    1.055212
------------------------------------------------------------------------------

Simultaneous results for frelimo2_3_2a, frelimo2_3_3a

                                                  Number of obs   =       1034

                                          (Std. Err. adjusted for 161 clusters in ea)
-------------------------------------------------------------------------------------
                    |               Robust
                    |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
frelimo2_3_2a_mean  |
          civiceduc |   .0600074   .0304727     1.97   0.049      .000282    .1197327
            hotline |   .0749102   .0300251     2.49   0.013     .0160622    .1337582
            verdade |   .0175025   .0391667     0.45   0.655    -.0592628    .0942679
                pr1 |   -.141201   .0630759    -2.24   0.025    -.2648275   -.0175745
                pr2 |  -.1355867   .0768557    -1.76   0.078     -.286221    .0150477
                pr3 |  -.0697869   .0804601    -0.87   0.386    -.2274858    .0879119
               post |   .0342688    .052025     0.66   0.510    -.0676983    .1362359
          post_miss |   .0171044   .0447161     0.38   0.702    -.0705375    .1047463
             health |   .0133716    .022874     0.58   0.559    -.0314607    .0582038
        health_miss |   .0794806   .0743171     1.07   0.285    -.0661782    .2251394
             police |  -.0341908   .0416298    -0.82   0.411    -.1157838    .0474021
        police_miss |   -.180154   .1366375    -1.32   0.187    -.4479586    .0876506
                sex |   .0372544    .023854     1.56   0.118    -.0094985    .0840074
                age |  -.0010033   .0011178    -0.90   0.369    -.0031941    .0011874
             single |   -.058703   .0327165    -1.79   0.073    -.1228261    .0054201
              divor |   .0277867   .1289545     0.22   0.829    -.2249594    .2805329
            norelig |  -.0335827     .06206    -0.54   0.588    -.1552181    .0880526
            protest |  -.0087096   .0349186    -0.25   0.803    -.0771488    .0597296
                com |  -.0096898   .0539463    -0.18   0.857    -.1154227     .096043
               prof |   .1317417   .0308685     4.27   0.000     .0712405    .1922429
            comform |   -.039588    .106465    -0.37   0.710    -.2482555    .1690796
           econfood |   -.013084   .0110674    -1.18   0.237    -.0347757    .0086077
              house |   .0532627   .0409984     1.30   0.194    -.0270927    .1336182
               oven |   .0656494   .0425626     1.54   0.123    -.0177718    .1490707
             lchang |   .0329053   .0766609     0.43   0.668    -.1173474     .183158
             llomue |    .010255   .0648116     0.16   0.874    -.1167734    .1372835
            lchuabo |   -.004288   .0588059    -0.07   0.942    -.1195454    .1109693
           lchitewe |   .0544075   .1446833     0.38   0.707    -.2291666    .3379817
             lronga |  -.0359406   .0489755    -0.73   0.463    -.1319309    .0600497
            chitsua |   .0808465   .0970378     0.83   0.405    -.1093442    .2710372
             living |    .036784   .0125478     2.93   0.003     .0121908    .0613771
              _cons |   .7615473    .061156    12.45   0.000     .6416837     .881411
--------------------+----------------------------------------------------------------
frelimo2_3_2a_lnvar |
              _cons |  -2.114127   .0668053   -31.65   0.000    -2.245063   -1.983191
--------------------+----------------------------------------------------------------
frelimo2_3_3a_mean  |
          civiceduc |   .0443121   .0669321     0.66   0.508    -.0868725    .1754967
            hotline |   .0494173   .0409595     1.21   0.228    -.0308619    .1296965
            verdade |   .1020275   .0450478     2.26   0.024     .0137353    .1903196
                pr1 |   .1222566   .0670736     1.82   0.068    -.0092051    .2537184
                pr2 |   .0062049   .1036014     0.06   0.952    -.1968501    .2092598
                pr3 |   .1541348    .106813     1.44   0.149    -.0552148    .3634844
               post |   .0434938    .055554     0.78   0.434    -.0653899    .1523775
          post_miss |   .0012099    .038771     0.03   0.975    -.0747799    .0771997
             health |  -.0235898   .0377064    -0.63   0.532    -.0974929    .0503133
        health_miss |   .2374883   .0585093     4.06   0.000     .1228123    .3521643
             police |   .0244538   .0470886     0.52   0.604    -.0678381    .1167458
        police_miss |  -.2648234   .1541749    -1.72   0.086    -.5670006    .0373539
                sex |  -.0292041    .037415    -0.78   0.435    -.1025362    .0441279
                age |  -.0013772   .0021408    -0.64   0.520    -.0055732    .0028187
             single |  -.0613881   .0495742    -1.24   0.216    -.1585517    .0357754
              divor |  -.2688653   .2594857    -1.04   0.300     -.777448    .2397174
            norelig |  -.0884809   .1191727    -0.74   0.458    -.3220551    .1450933
            protest |  -.0436892   .0574796    -0.76   0.447    -.1563471    .0689687
                com |  -.1004059   .1198992    -0.84   0.402    -.3354039    .1345922
               prof |   .0359865   .0988042     0.36   0.716    -.1576663    .2296392
            comform |  -.4905011   .2216568    -2.21   0.027    -.9249405   -.0560617
           econfood |  -.0053344   .0116275    -0.46   0.646    -.0281238     .017455
              house |  -.0038603   .0568214    -0.07   0.946    -.1152281    .1075075
               oven |  -.0251993    .079882    -0.32   0.752    -.1817651    .1313664
             lchang |  -.1207329   .0971677    -1.24   0.214     -.311178    .0697123
             llomue |  -.1536571   .0679157    -2.26   0.024    -.2867694   -.0205447
            lchuabo |  -.1762742   .0758141    -2.33   0.020    -.3248672   -.0276813
           lchitewe |  -.2878614   .2007391    -1.43   0.152    -.6813028      .10558
             lronga |   .0368524   .0767713     0.48   0.631    -.1136166    .1873214
            chitsua |   .1977512   .0762365     2.59   0.009     .0483304     .347172
             living |     .02778   .0202048     1.37   0.169    -.0118206    .0673806
              _cons |   .8666268   .1016116     8.53   0.000     .6674717    1.065782
--------------------+----------------------------------------------------------------
frelimo2_3_3a_lnvar |
              _cons |  -2.034821   .0884057   -23.02   0.000    -2.208093   -1.861549
-------------------------------------------------------------------------------------

 ( 1)  [frelimo2_3_2a_mean]civiceduc - [frelimo2_3_3a_mean]civiceduc = 0

           chi2(  1) =    0.07
         Prob > chi2 =    0.7974
.79742138

 ( 1)  [frelimo2_3_2a_mean]hotline - [frelimo2_3_3a_mean]hotline = 0

           chi2(  1) =    0.45
         Prob > chi2 =    0.5024
.50243804

 ( 1)  [frelimo2_3_2a_mean]verdade - [frelimo2_3_3a_mean]verdade = 0

           chi2(  1) =    2.57
         Prob > chi2 =    0.1092
.10915411
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_22 omitted because of collinearity

Linear regression                                      Number of obs =    1031
                                                       F( 54,   160) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.1130
                                                       Root MSE      =  .34183

                                     (Std. Err. adjusted for 161 clusters in ea)
--------------------------------------------------------------------------------
               |               Robust
      frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |   .0563457   .0326098     1.73   0.086    -.0080555    .1207469
       hotline |   .0685652   .0298198     2.30   0.023      .009674    .1274563
       verdade |   .0267345     .03716     0.72   0.473    -.0466527    .1001218
           pr1 |  -.0440484    .114472    -0.38   0.701    -.2701193    .1820226
           pr2 |   .0877366   .0880988     1.00   0.321    -.0862499     .261723
           pr3 |   .0911485   .1200842     0.76   0.449    -.1460059    .3283029
          post |   .0349039   .0459861     0.76   0.449    -.0559142    .1257219
     post_miss |   .0375061   .0482078     0.78   0.438    -.0576995    .1327117
        health |  -.0020901   .0222732    -0.09   0.925    -.0460774    .0418973
   health_miss |   .1089025   .0626571     1.74   0.084    -.0148391    .2326442
        police |  -.0208736   .0376747    -0.55   0.580    -.0952775    .0535303
   police_miss |   -.266069   .1332908    -2.00   0.048    -.5293052   -.0028328
           sex |   .0152486   .0214528     0.71   0.478    -.0271186    .0576158
           age |  -.0005263   .0010438    -0.50   0.615    -.0025877    .0015351
        single |  -.0418644   .0329658    -1.27   0.206    -.1069686    .0232397
         divor |  -.0597255   .1216825    -0.49   0.624    -.3000366    .1805855
       norelig |  -.0552602   .0601033    -0.92   0.359    -.1739583     .063438
       protest |  -.0224348   .0326129    -0.69   0.493     -.086842    .0419725
           com |  -.0156171   .0517212    -0.30   0.763    -.1177614    .0865273
          prof |   .0339196   .0622935     0.54   0.587     -.089104    .1569432
       comform |  -.1116799   .1200058    -0.93   0.353    -.3486796    .1253198
      econfood |  -.0083779   .0112272    -0.75   0.457    -.0305505    .0137947
         house |   .0773571    .040319     1.92   0.057     -.002269    .1569832
          oven |   .0494664   .0396314     1.25   0.214    -.0288017    .1277344
        lchang |  -.0095211    .067818    -0.14   0.889    -.1434549    .1244127
        llomue |   .0006573   .0651329     0.01   0.992    -.1279738    .1292884
       lchuabo |  -.0019918   .0583464    -0.03   0.973    -.1172201    .1132366
      lchitewe |  -.0183889    .139963    -0.13   0.896    -.2948021    .2580244
        lronga |  -.0355022   .0479054    -0.74   0.460    -.1301107    .0591064
       chitsua |   .1045764   .0853071     1.23   0.222    -.0638968    .2730495
        living |   .0290041    .013036     2.22   0.027     .0032593    .0547489
 _Iinterview_2 |   .0097809   .0363347     0.27   0.788    -.0619766    .0815384
 _Iinterview_3 |  -.0946102   .1035561    -0.91   0.362    -.2991234    .1099029
 _Iinterview_4 |  -.1342723   .0869871    -1.54   0.125    -.3060633    .0375188
 _Iinterview_5 |  -.0232353   .0888021    -0.26   0.794    -.1986108    .1521402
 _Iinterview_6 |  -.1803193    .110476    -1.63   0.105    -.3984984    .0378599
 _Iinterview_7 |  -.1566387   .0833562    -1.88   0.062    -.3212591    .0079816
 _Iinterview_8 |  -.0801875   .0968263    -0.83   0.409    -.2714098    .1110349
 _Iinterview_9 |  -.2386016   .0900112    -2.65   0.009    -.4163648   -.0608384
_Iinterview_10 |  -.0664543   .0722257    -0.92   0.359    -.2090929    .0761843
_Iinterview_11 |  -.2777151   .0822752    -3.38   0.001    -.4402006   -.1152296
_Iinterview_12 |  -.0776725   .0908943    -0.85   0.394    -.2571798    .1018348
_Iinterview_13 |  -.2046499    .079816    -2.56   0.011    -.3622788   -.0470211
_Iinterview_14 |  -.1283686   .0860087    -1.49   0.138    -.2982272    .0414901
_Iinterview_15 |   .0570022   .1386363     0.41   0.682    -.2167909    .3307953
_Iinterview_16 |  -.1476684   .1374559    -1.07   0.284    -.4191303    .1237934
_Iinterview_17 |   -.229447   .1299721    -1.77   0.079     -.486129     .027235
_Iinterview_18 |  -.0049934   .1305767    -0.04   0.970    -.2628696    .2528827
_Iinterview_19 |  -.1879654   .1288429    -1.46   0.147    -.4424174    .0664866
_Iinterview_20 |  -.1174611    .137629    -0.85   0.395    -.3892648    .1543426
_Iinterview_21 |  -.0529953   .0540443    -0.98   0.328    -.1597274    .0537369
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |   .0266604   .0455582     0.59   0.559    -.0633125    .1166333
_Iinterview_24 |   .0361285   .0321917     1.12   0.263     -.027447     .099704
_Iinterview_25 |  -.3290151   .0917591    -3.59   0.000    -.5102303   -.1477998
_Iinterview_26 |  -.2992769   .1117597    -2.68   0.008    -.5199912   -.0785626
_Iinterview_27 |  -.0312037   .0595329    -0.52   0.601    -.1487753     .086368
_Iinterview_28 |   .1366217   .1557578     0.88   0.382    -.1709847    .4442281
         _cons |   .7838323    .066315    11.82   0.000     .6528666     .914798
--------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    frelimo2 |       250        .824    .3815841          0          1
.824

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.19
            Prob > F =    0.6639
.66393524

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.71
            Prob > F =    0.4007
.40074161

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    1.71
            Prob > F =    0.1933
.19327733

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    2.05
            Prob > F =    0.1087
.10865666
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_22 omitted because of collinearity

Linear regression                                      Number of obs =     874
                                                       F( 53,   160) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.1206
                                                       Root MSE      =  .34425

                                     (Std. Err. adjusted for 161 clusters in ea)
--------------------------------------------------------------------------------
               |               Robust
      frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |   .0553736   .0314111     1.76   0.080    -.0066602    .1174074
       hotline |   .0737314   .0305907     2.41   0.017     .0133177    .1341451
       verdade |   .0173266   .0412636     0.42   0.675     -.064165    .0988181
           pr1 |  -.0135383    .120084    -0.11   0.910    -.2506925    .2236158
           pr2 |   .0753877   .1110343     0.68   0.498     -.143894    .2946694
           pr3 |   .0968604    .147724     0.66   0.513    -.1948799    .3886007
          post |   .0293759   .0541976     0.54   0.589    -.0776591    .1364109
     post_miss |   .0319871   .0517446     0.62   0.537    -.0702034    .1341776
        health |   .0020031   .0240562     0.08   0.934    -.0455055    .0495117
   health_miss |   .0857538   .0687024     1.25   0.214    -.0499267    .2214342
        police |  -.0210426   .0432575    -0.49   0.627    -.1064718    .0643866
   police_miss |  -.2248217   .1409143    -1.60   0.113    -.5031136    .0534701
           sex |   .0360713   .0250749     1.44   0.152    -.0134492    .0855918
           age |  -.0006253   .0011129    -0.56   0.575    -.0028233    .0015726
        single |  -.0542434    .036562    -1.48   0.140    -.1264498    .0179629
         divor |   .0073704   .1198059     0.06   0.951    -.2292345    .2439753
       norelig |  -.0314501   .0620537    -0.51   0.613        -.154    .0910999
       protest |  -.0117354   .0368292    -0.32   0.750    -.0844694    .0609987
           com |   .0164496   .0540838     0.30   0.761    -.0903606    .1232598
          prof |   .0924713   .0372971     2.48   0.014     .0188132    .1661294
       comform |  -.0134712   .1112015    -0.12   0.904    -.2330832    .2061408
      econfood |  -.0060848   .0121883    -0.50   0.618    -.0301554    .0179858
         house |   .0684178    .044306     1.54   0.125    -.0190822    .1559177
          oven |   .0629499   .0414738     1.52   0.131    -.0189568    .1448566
        lchang |   .0424759   .0771607     0.55   0.583    -.1099089    .1948607
        llomue |   .0163515   .0683516     0.24   0.811    -.1186362    .1513392
       lchuabo |   .0043994    .058134     0.08   0.940    -.1104097    .1192084
      lchitewe |   .0825281   .1572667     0.52   0.600    -.2280582    .3931144
        lronga |  -.0396206   .0553183    -0.72   0.475    -.1488689    .0696277
       chitsua |   .1171167   .1024643     1.14   0.255    -.0852403    .3194736
        living |   .0370929   .0139472     2.66   0.009     .0095485    .0646373
 _Iinterview_2 |  -.0217117   .0414248    -0.52   0.601    -.1035216    .0600982
 _Iinterview_3 |  -.1753154   .1198712    -1.46   0.146    -.4120493    .0614185
 _Iinterview_4 |  -.2147736   .1049862    -2.05   0.042    -.4221111   -.0074361
 _Iinterview_5 |  -.1033299   .1091175    -0.95   0.345    -.3188262    .1121664
 _Iinterview_6 |  -.3015439   .1329491    -2.27   0.025    -.5641054   -.0389825
 _Iinterview_7 |  -.2558159   .1046524    -2.44   0.016    -.4624941   -.0491376
 _Iinterview_8 |  -.1755569    .118295    -1.48   0.140    -.4091779    .0580641
 _Iinterview_9 |  -.2997214    .102732    -2.92   0.004     -.502607   -.0968358
_Iinterview_10 |  -.1188154   .1016507    -1.17   0.244    -.3195655    .0819347
_Iinterview_11 |  -.3671463   .0971255    -3.78   0.000    -.5589596    -.175333
_Iinterview_12 |  -.1835989   .1112742    -1.65   0.101    -.4033545    .0361568
_Iinterview_13 |   -.243476   .0903823    -2.69   0.008     -.421972   -.0649799
_Iinterview_14 |  -.2069105   .1050757    -1.97   0.051    -.4144247    .0006036
_Iinterview_15 |  -.0009529   .1462992    -0.01   0.995    -.2898795    .2879737
_Iinterview_16 |  -.2149261   .1435695    -1.50   0.136    -.4984617    .0686096
_Iinterview_17 |  -.2633646   .1420516    -1.85   0.066    -.5439026    .0171734
_Iinterview_18 |  -.0821554   .1389865    -0.59   0.555    -.3566401    .1923294
_Iinterview_19 |  -.2645798   .1445985    -1.83   0.069    -.5501476    .0209881
_Iinterview_20 |  -.1678711   .1511999    -1.11   0.269    -.4664761    .1307339
_Iinterview_21 |  -.0491855   .0582748    -0.84   0.400    -.1642724    .0659014
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |  -.0040228   .0491271    -0.08   0.935    -.1010439    .0929983
_Iinterview_24 |   .0037667   .0367599     0.10   0.919    -.0688304    .0763639
_Iinterview_25 |  -.3582261   .0967415    -3.70   0.000    -.5492811   -.1671712
_Iinterview_26 |  -.3178227    .115885    -2.74   0.007    -.5466842   -.0889612
_Iinterview_27 |     -.0259   .0558936    -0.46   0.644    -.1362844    .0844844
_Iinterview_28 |   .0853089   .1644971     0.52   0.605    -.2395566    .4101745
         _cons |   .7767935   .0709278    10.95   0.000      .636718     .916869
--------------------------------------------------------------------------------
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_22 omitted because of collinearity

      Source |       SS       df       MS              Number of obs =     874
-------------+------------------------------           F( 57,   816) =    1.96
       Model |   13.259638    57  .232625228           Prob > F      =  0.0000
    Residual |  96.7003162   816   .11850529           R-squared     =  0.1206
-------------+------------------------------           Adj R-squared =  0.0592
       Total |  109.959954   873   .12595642           Root MSE      =  .34425

--------------------------------------------------------------------------------
      frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |   .0553736   .0344852     1.61   0.109    -.0123166    .1230637
       hotline |   .0737314   .0340881     2.16   0.031     .0068207    .1406421
       verdade |   .0173266   .0353932     0.49   0.625    -.0521459     .086799
           pr1 |  -.0135383    .254419    -0.05   0.958    -.5129312    .4858545
           pr2 |   .0753877   .3614841     0.21   0.835    -.6341605    .7849359
           pr3 |   .0968604   .4011117     0.24   0.809     -.690472    .8841928
          post |   .0293759   .0494374     0.59   0.553    -.0676636    .1264154
     post_miss |   .0319871   .0790777     0.40   0.686    -.1232327    .1872068
        health |   .0020031   .0293856     0.07   0.946    -.0556771    .0596833
   health_miss |   .0857538   .0958205     0.89   0.371    -.1023299    .2738374
        police |  -.0210426   .0367101    -0.57   0.567    -.0930999    .0510147
   police_miss |  -.2248217   .1490005    -1.51   0.132    -.5172911    .0676476
           sex |   .0360713   .0255005     1.41   0.158     -.013983    .0861256
           age |  -.0006253   .0009921    -0.63   0.529    -.0025728    .0013221
        single |  -.0542434   .0348148    -1.56   0.120    -.1225805    .0140936
         divor |   .0073704   .1344213     0.05   0.956    -.2564819    .2712226
       norelig |  -.0314501   .0622222    -0.51   0.613    -.1535845    .0906844
       protest |  -.0117354   .0303755    -0.39   0.699    -.0713588     .047888
           com |   .0164496   .0568908     0.29   0.773      -.09522    .1281193
          prof |   .0924713   .1003507     0.92   0.357    -.1045046    .2894472
       comform |  -.0134712    .115236    -0.12   0.907    -.2396651    .2127227
      econfood |  -.0060848   .0112009    -0.54   0.587    -.0280708    .0159011
         house |   .0684178   .0398852     1.72   0.087    -.0098719    .1467074
          oven |   .0629499   .0478847     1.31   0.189    -.0310418    .1569417
        lchang |   .0424759   .0640971     0.66   0.508    -.0833387    .1682905
        llomue |   .0163515    .052355     0.31   0.755    -.0864149     .119118
       lchuabo |   .0043994   .0491185     0.09   0.929    -.0920141    .1008128
      lchitewe |   .0825281   .1370778     0.60   0.547    -.1865385    .3515947
        lronga |  -.0396206   .0522441    -0.76   0.448    -.1421692     .062928
       chitsua |   .1171167   .1157717     1.01   0.312    -.1101287     .344362
        living |   .0370929   .0121141     3.06   0.002     .0133144    .0608713
 _Iinterview_2 |  -.0217117   .1639682    -0.13   0.895    -.3435607    .3001374
 _Iinterview_3 |  -.1753154   .3991581    -0.44   0.661    -.9588131    .6081824
 _Iinterview_4 |  -.2147736   .3945946    -0.54   0.586    -.9893136    .5597665
 _Iinterview_5 |  -.1033299   .5258497    -0.20   0.844    -1.135507    .9288475
 _Iinterview_6 |  -.3015439   .4022115    -0.75   0.454    -1.091035    .4879471
 _Iinterview_7 |  -.2558159   .3961634    -0.65   0.519    -1.033435    .5218036
 _Iinterview_8 |  -.1755569   .4001737    -0.44   0.661     -.961048    .6099342
 _Iinterview_9 |  -.2997214   .3630909    -0.83   0.409    -1.012424    .4129808
_Iinterview_10 |  -.1188154   .3883553    -0.31   0.760    -.8811086    .6434777
_Iinterview_11 |  -.3671463   .3637922    -1.01   0.313    -1.081225    .3469325
_Iinterview_12 |  -.1835989   .3667602    -0.50   0.617    -.9035034    .5363057
_Iinterview_13 |   -.243476   .3637491    -0.67   0.503    -.9574702    .4705182
_Iinterview_14 |  -.2069105   .3656632    -0.57   0.572    -.9246619    .5108408
_Iinterview_15 |  -.0009529   .2709228    -0.00   0.997    -.5327407    .5308348
_Iinterview_16 |  -.2149261   .2640495    -0.81   0.416    -.7332224    .3033702
_Iinterview_17 |  -.2633646   .2615305    -1.01   0.314    -.7767164    .2499872
_Iinterview_18 |  -.0821554    .261078    -0.31   0.753    -.5946189    .4303082
_Iinterview_19 |  -.2645798   .2614361    -1.01   0.312    -.7777462    .2485867
_Iinterview_20 |  -.1678711   .2562116    -0.66   0.513    -.6707827    .3350404
_Iinterview_21 |  -.0491855    .077678    -0.63   0.527    -.2016577    .1032866
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |  -.0040228    .075079    -0.05   0.957    -.1513935    .1433479
_Iinterview_24 |   .0037667   .0729942     0.05   0.959    -.1395118    .1470453
_Iinterview_25 |  -.3582261   .1662038    -2.16   0.031    -.6844634   -.0319889
_Iinterview_26 |  -.3178227   .0984807    -3.23   0.001     -.511128   -.1245174
_Iinterview_27 |     -.0259   .0892531    -0.29   0.772    -.2010926    .1492926
_Iinterview_28 |   .0853089   .4336267     0.20   0.844    -.7658463    .9364642
         _cons |   .7767935   .0811673     9.57   0.000     .6174722    .9361148
--------------------------------------------------------------------------------
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_5 omitted because of collinearity
note: _Iinterview_20 omitted because of collinearity
note: _Iinterview_22 omitted because of collinearity
note: _Iinterview_28 omitted because of collinearity

Linear regression                                      Number of obs =     407
                                                       F( 51,   143) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.1724
                                                       Root MSE      =  .35835

                                     (Std. Err. adjusted for 144 clusters in ea)
--------------------------------------------------------------------------------
               |               Robust
      frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |   .0499289   .0695922     0.72   0.474    -.0876335    .1874912
       hotline |   .0392891    .043737     0.90   0.371    -.0471655    .1257437
       verdade |   .0806904   .0454779     1.77   0.078    -.0092053    .1705862
           pr1 |  -.0296897     .17148    -0.17   0.863    -.3686529    .3092735
           pr2 |   .1341904   .1906555     0.70   0.483    -.2426768    .5110576
           pr3 |    .139281   .2696864     0.52   0.606     -.393806     .672368
          post |    .040237   .0526099     0.76   0.446    -.0637565    .1442305
     post_miss |   .0036325   .0388222     0.09   0.926     -.073107     .080372
        health |   -.018047   .0392781    -0.46   0.647    -.0956877    .0595937
   health_miss |   .3149434   .0638368     4.93   0.000     .1887578    .4411291
        police |   .0296168    .043062     0.69   0.493    -.0555035    .1147371
   police_miss |  -.3430836   .1470291    -2.33   0.021    -.6337148   -.0524524
           sex |  -.0401457     .03846    -1.04   0.298    -.1161694    .0358779
           age |  -.0014282   .0021142    -0.68   0.500    -.0056073    .0027509
        single |   -.015634   .0604914    -0.26   0.796    -.1352069    .1039389
         divor |  -.2377327   .2650974    -0.90   0.371    -.7617487    .2862833
       norelig |  -.0704612   .1151846    -0.61   0.542    -.2981458    .1572234
       protest |  -.0217065   .0650556    -0.33   0.739    -.1503014    .1068883
           com |  -.0699648    .108758    -0.64   0.521    -.2849459    .1450162
          prof |  -.0596889   .0982963    -0.61   0.545    -.2539904    .1346127
       comform |  -.4518913   .1976672    -2.29   0.024    -.8426185   -.0611641
      econfood |  -.0007801    .014997    -0.05   0.959    -.0304245    .0288644
         house |   .0398331   .0709008     0.56   0.575    -.1003159    .1799821
          oven |  -.0245996   .0738906    -0.33   0.740    -.1706585    .1214593
        lchang |  -.1019018   .1052688    -0.97   0.335    -.3099858    .1061823
        llomue |  -.1181573   .0710866    -1.66   0.099    -.2586735     .022359
       lchuabo |  -.1571635   .0864727    -1.82   0.071    -.3280933    .0137663
      lchitewe |  -.2619135   .1916249    -1.37   0.174    -.6406969    .1168699
        lronga |   .0070619   .0841413     0.08   0.933    -.1592595    .1733833
       chitsua |    .088878   .1124999     0.79   0.431    -.1334997    .3112558
        living |   .0288468   .0211956     1.36   0.176    -.0130503    .0707439
 _Iinterview_2 |    .087527   .0707994     1.24   0.218    -.0524216    .2274757
 _Iinterview_3 |  -.1114695   .2450249    -0.45   0.650    -.5958082    .3728693
 _Iinterview_4 |  -.1070334   .2137417    -0.50   0.617    -.5295349    .3154681
 _Iinterview_5 |          0  (omitted)
 _Iinterview_6 |  -.1364878   .2642887    -0.52   0.606    -.6589052    .3859296
 _Iinterview_7 |  -.0190219   .2076452    -0.09   0.927    -.4294725    .3914288
 _Iinterview_8 |  -.0579976   .2286097    -0.25   0.800    -.5098886    .3938934
 _Iinterview_9 |  -.3202361   .2003418    -1.60   0.112    -.7162501    .0757779
_Iinterview_10 |  -.0272661   .1117992    -0.24   0.808    -.2482587    .1937265
_Iinterview_11 |  -.2471048   .2016705    -1.23   0.222    -.6457453    .1515356
_Iinterview_12 |   .0247794   .1481246     0.17   0.867    -.2680174    .3175761
_Iinterview_13 |  -.3625468   .1419357    -2.55   0.012    -.6431099   -.0819837
_Iinterview_14 |  -.1557824   .1938686    -0.80   0.423     -.539001    .2274361
_Iinterview_15 |    .236216   .1493864     1.58   0.116    -.0590749    .5315069
_Iinterview_16 |   .0638575   .1640353     0.39   0.698    -.2603898    .3881048
_Iinterview_17 |  -.1033795   .1731042    -0.60   0.551    -.4455531    .2387941
_Iinterview_18 |   .1795533   .1515806     1.18   0.238    -.1200748    .4791814
_Iinterview_19 |   .0032627   .1590013     0.02   0.984     -.311034    .3175594
_Iinterview_20 |          0  (omitted)
_Iinterview_21 |  -.2141381   .1086132    -1.97   0.051    -.4288329    .0005567
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |  -.0306751   .1142936    -0.27   0.789    -.2565983    .1952481
_Iinterview_24 |  -.0062088   .0629622    -0.10   0.922    -.1306657    .1182481
_Iinterview_25 |  -.4232274   .0884571    -4.78   0.000    -.5980798    -.248375
_Iinterview_26 |  -.1080367   .1084763    -1.00   0.321     -.322461    .1063876
_Iinterview_27 |   -.032026   .1383997    -0.23   0.817    -.3055996    .2415476
_Iinterview_28 |          0  (omitted)
         _cons |   .8870993   .1165995     7.61   0.000     .6566181    1.117581
--------------------------------------------------------------------------------
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_5 omitted because of collinearity
note: _Iinterview_20 omitted because of collinearity
note: _Iinterview_22 omitted because of collinearity
note: _Iinterview_28 omitted because of collinearity

      Source |       SS       df       MS              Number of obs =     407
-------------+------------------------------           F( 54,   352) =    1.36
       Model |  9.41729622    54  .174394374           Prob > F      =  0.0565
    Residual |  45.2018684   352  .128414399           R-squared     =  0.1724
-------------+------------------------------           Adj R-squared =  0.0455
       Total |  54.6191646   406  .134529962           Root MSE      =  .35835

--------------------------------------------------------------------------------
      frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |   .0499289   .0615707     0.81   0.418    -.0711639    .1710216
       hotline |   .0392891   .0573354     0.69   0.494    -.0734739    .1520521
       verdade |   .0806904   .0634289     1.27   0.204    -.0440568    .2054377
           pr1 |  -.0296897   .1455721    -0.20   0.839    -.3159901    .2566107
           pr2 |   .1341904   .5309679     0.25   0.801     -.910078    1.178459
           pr3 |    .139281   .6586528     0.21   0.833    -1.156109    1.434671
          post |    .040237   .0675504     0.60   0.552    -.0926162    .1730902
     post_miss |   .0036325   .0970498     0.04   0.970    -.1872378    .1945028
        health |   -.018047   .0493911    -0.37   0.715    -.1151858    .0790919
   health_miss |   .3149434   .1742064     1.81   0.071     -.027673    .6575598
        police |   .0296168   .0571012     0.52   0.604    -.0826857    .1419193
   police_miss |  -.3430836   .2595309    -1.32   0.187    -.8535099    .1673427
           sex |  -.0401457    .039341    -1.02   0.308    -.1175187    .0372272
           age |  -.0014282   .0016396    -0.87   0.384    -.0046529    .0017965
        single |   -.015634   .0524578    -0.30   0.766     -.118804     .087536
         divor |  -.2377327   .2205141    -1.08   0.282    -.6714235    .1959581
       norelig |  -.0704612   .1115598    -0.63   0.528    -.2898689    .1489465
       protest |  -.0217065   .0506033    -0.43   0.668    -.1212293    .0778162
           com |  -.0699648    .104228    -0.67   0.502    -.2749528    .1350232
          prof |  -.0596889   .1394647    -0.43   0.669    -.3339778    .2146001
       comform |  -.4518913   .1565754    -2.89   0.004    -.7598322   -.1439504
      econfood |  -.0007801   .0182455    -0.04   0.966    -.0366639    .0351038
         house |   .0398331   .0630264     0.63   0.528    -.0841227    .1637888
          oven |  -.0245996   .0803976    -0.31   0.760    -.1827196    .1335204
        lchang |  -.1019018   .0981084    -1.04   0.300    -.2948541    .0910506
        llomue |  -.1181573   .0885482    -1.33   0.183    -.2923074    .0559928
       lchuabo |  -.1571635   .0825912    -1.90   0.058    -.3195978    .0052707
      lchitewe |  -.2619135   .1764156    -1.48   0.139    -.6088748    .0850478
        lronga |   .0070619   .0752113     0.09   0.925     -.140858    .1549819
       chitsua |    .088878   .1954417     0.45   0.650    -.2955023    .4732583
        living |   .0288468   .0184716     1.56   0.119    -.0074818    .0651754
 _Iinterview_2 |    .087527   .2672372     0.33   0.743    -.4380553    .6131094
 _Iinterview_3 |  -.1114695   .6552746    -0.17   0.865    -1.400215    1.177276
 _Iinterview_4 |  -.1070334   .6531797    -0.16   0.870    -1.391659    1.177592
 _Iinterview_5 |          0  (omitted)
 _Iinterview_6 |  -.1364878   .6630687    -0.21   0.837    -1.440562    1.167587
 _Iinterview_7 |  -.0190219   .6455652    -0.03   0.977    -1.288672    1.250628
 _Iinterview_8 |  -.0579976   .6559267    -0.09   0.930    -1.348026    1.232031
 _Iinterview_9 |  -.3202361   .5305728    -0.60   0.547    -1.363728    .7232553
_Iinterview_10 |  -.0272661   .3730489    -0.07   0.942    -.7609512     .706419
_Iinterview_11 |  -.2471048   .5342072    -0.46   0.644    -1.297744    .8035345
_Iinterview_12 |   .0247794   .5326529     0.05   0.963    -1.022803    1.072362
_Iinterview_13 |  -.3625468   .5267222    -0.69   0.492    -1.398465    .6733715
_Iinterview_14 |  -.1557824   .5299355    -0.29   0.769     -1.19802    .8864556
_Iinterview_15 |    .236216   .1690586     1.40   0.163    -.0962761     .568708
_Iinterview_16 |   .0638575   .1334745     0.48   0.633    -.1986504    .3263654
_Iinterview_17 |  -.1033795   .1307186    -0.79   0.430    -.3604671    .1537081
_Iinterview_18 |   .1795533   .1274214     1.41   0.160    -.0710497    .4301562
_Iinterview_19 |   .0032627   .1260844     0.03   0.979    -.2447108    .2512362
_Iinterview_20 |          0  (omitted)
_Iinterview_21 |  -.2141381   .1180463    -1.81   0.071    -.4463029    .0180268
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |  -.0306751   .1174869    -0.26   0.794    -.2617396    .2003895
_Iinterview_24 |  -.0062088   .1101861    -0.06   0.955    -.2229147    .2104972
_Iinterview_25 |  -.4232274   .2025256    -2.09   0.037    -.8215399    -.024915
_Iinterview_26 |  -.1080367   .1704065    -0.63   0.526    -.4431796    .2271061
_Iinterview_27 |   -.032026   .1462926    -0.22   0.827    -.3197435    .2556915
_Iinterview_28 |          0  (omitted)
         _cons |   .8870993   .1224238     7.25   0.000     .6463253    1.127873
--------------------------------------------------------------------------------

Simultaneous results for frelimo2_4_2a, frelimo2_4_3a

                                                  Number of obs   =       1031

                                          (Std. Err. adjusted for 161 clusters in ea)
-------------------------------------------------------------------------------------
                    |               Robust
                    |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
frelimo2_4_2a_mean  |
          civiceduc |   .0553736   .0303683     1.82   0.068    -.0041473    .1148944
            hotline |   .0737314   .0295752     2.49   0.013     .0157651    .1316978
            verdade |   .0173266   .0398938     0.43   0.664    -.0608638    .0955169
                pr1 |  -.0135383   .1160976    -0.12   0.907    -.2410854    .2140087
                pr2 |   .0753877   .1073483     0.70   0.483     -.135011    .2857864
                pr3 |   .0968604     .14282     0.68   0.498    -.1830616    .3767824
               post |   .0293759   .0523984     0.56   0.575    -.0733231    .1320749
          post_miss |   .0319871   .0500268     0.64   0.523    -.0660637    .1300378
             health |   .0020031   .0232576     0.09   0.931    -.0435809    .0475871
        health_miss |   .0857538   .0664217     1.29   0.197    -.0444304    .2159379
             police |  -.0210426   .0418214    -0.50   0.615    -.1030111    .0609259
        police_miss |  -.2248217   .1362363    -1.65   0.099    -.4918401    .0421966
                sex |   .0360713   .0242425     1.49   0.137    -.0114431    .0835857
                age |  -.0006253    .001076    -0.58   0.561    -.0027342    .0014835
             single |  -.0542434   .0353483    -1.53   0.125    -.1235247    .0150379
              divor |   .0073704   .1158287     0.06   0.949    -.2196497    .2343905
            norelig |  -.0314501   .0599937    -0.52   0.600    -.1490355    .0861354
            protest |  -.0117354   .0356066    -0.33   0.742     -.081523    .0580522
                com |   .0164496   .0522884     0.31   0.753    -.0860337     .118933
               prof |   .0924713    .036059     2.56   0.010     .0217971    .1631456
            comform |  -.0134712   .1075099    -0.13   0.900    -.2241868    .1972444
           econfood |  -.0060848   .0117836    -0.52   0.606    -.0291803    .0170107
              house |   .0684178   .0428352     1.60   0.110    -.0155376    .1523731
               oven |   .0629499    .040097     1.57   0.116    -.0156388    .1415386
             lchang |   .0424759   .0745992     0.57   0.569    -.1037359    .1886876
             llomue |   .0163515   .0660825     0.25   0.805    -.1131679    .1458709
            lchuabo |   .0043994   .0562042     0.08   0.938    -.1057588    .1145575
           lchitewe |   .0825281   .1520459     0.54   0.587    -.2154764    .3805326
             lronga |  -.0396206   .0534819    -0.74   0.459    -.1444433    .0652021
            chitsua |   .1171167   .0990628     1.18   0.237    -.0770428    .3112761
             living |   .0370929   .0134842     2.75   0.006     .0106643    .0635215
      _Iinterview_2 |  -.0217117   .0400496    -0.54   0.588    -.1002075    .0567841
      _Iinterview_3 |  -.1753154   .1158919    -1.51   0.130    -.4024593    .0518285
      _Iinterview_4 |  -.2147736    .101501    -2.12   0.034    -.4137119   -.0158352
      _Iinterview_5 |  -.1033299   .1054951    -0.98   0.327    -.3100966    .1034367
      _Iinterview_6 |  -.3015439   .1285356    -2.35   0.019    -.5534691   -.0496188
      _Iinterview_7 |  -.2558159   .1011783    -2.53   0.011    -.4541216   -.0575101
      _Iinterview_8 |  -.1755569    .114368    -1.54   0.125     -.399714    .0486002
      _Iinterview_9 |  -.2997214   .0993216    -3.02   0.003    -.4943882   -.1050546
     _Iinterview_10 |  -.1188154   .0982762    -1.21   0.227    -.3114332    .0738024
     _Iinterview_11 |  -.3671463   .0939012    -3.91   0.000    -.5511893   -.1831033
     _Iinterview_12 |  -.1835989   .1075802    -1.71   0.088    -.3944523    .0272545
     _Iinterview_13 |   -.243476   .0873818    -2.79   0.005    -.4147412   -.0722107
     _Iinterview_14 |  -.2069105   .1015875    -2.04   0.042    -.4060183   -.0078027
     _Iinterview_15 |  -.0009529   .1414425    -0.01   0.995    -.2781752    .2762693
     _Iinterview_16 |  -.2149261   .1388034    -1.55   0.122    -.4869758    .0571236
     _Iinterview_17 |  -.2633646   .1373359    -1.92   0.055    -.5325381    .0058089
     _Iinterview_18 |  -.0821554   .1343726    -0.61   0.541    -.3455208    .1812101
     _Iinterview_19 |  -.2645798   .1397983    -1.89   0.058    -.5385794    .0094198
     _Iinterview_20 |  -.1678711   .1461805    -1.15   0.251    -.4543797    .1186375
     _Iinterview_21 |  -.0491855   .0563402    -0.87   0.383    -.1596103    .0612393
     _Iinterview_22 |          0  (omitted)
     _Iinterview_23 |  -.0040228   .0474962    -0.08   0.933    -.0971136     .089068
     _Iinterview_24 |   .0037667   .0355396     0.11   0.916    -.0658895     .073423
     _Iinterview_25 |  -.3582261   .0935299    -3.83   0.000    -.5415415   -.1749108
     _Iinterview_26 |  -.3178227    .112038    -2.84   0.005    -.5374131   -.0982323
     _Iinterview_27 |     -.0259   .0540381    -0.48   0.632    -.1318128    .0800128
     _Iinterview_28 |   .0853089   .1590363     0.54   0.592    -.2263964    .3970143
              _cons |   .7767935   .0685732    11.33   0.000     .6423924    .9111946
--------------------+----------------------------------------------------------------
frelimo2_4_2a_lnvar |
              _cons |  -2.132798   .0634108   -33.63   0.000    -2.257081   -2.008515
--------------------+----------------------------------------------------------------
frelimo2_4_3a_mean  |
          civiceduc |   .0499289   .0647752     0.77   0.441    -.0770281    .1768859
            hotline |   .0392891   .0407096     0.97   0.334    -.0405003    .1190785
            verdade |   .0806904     .04233     1.91   0.057    -.0022748    .1636557
                pr1 |  -.0296897   .1596105    -0.19   0.852    -.3425206    .2831412
                pr2 |   .1341904   .1774587     0.76   0.450    -.2136222    .4820031
                pr3 |    .139281   .2510193     0.55   0.579    -.3527077    .6312697
               post |    .040237   .0489683     0.82   0.411    -.0557392    .1362131
          post_miss |   .0036325    .036135     0.10   0.920    -.0671908    .0744558
             health |   -.018047   .0365593    -0.49   0.622     -.089702     .053608
        health_miss |   .3149434   .0594181     5.30   0.000      .198486    .4314008
             police |   .0296168   .0400813     0.74   0.460    -.0489412    .1081748
        police_miss |  -.3430836    .136852    -2.51   0.012    -.6113086   -.0748586
                sex |  -.0401457   .0357979    -1.12   0.262    -.1103083    .0300169
                age |  -.0014282   .0019678    -0.73   0.468    -.0052851    .0024287
             single |   -.015634   .0563043    -0.28   0.781    -.1259885    .0947205
              divor |  -.2377327   .2467479    -0.96   0.335    -.7213498    .2458843
            norelig |  -.0704612   .1072118    -0.66   0.511    -.2805925    .1396701
            protest |  -.0217065   .0605526    -0.36   0.720    -.1403874    .0969743
                com |  -.0699648     .10123    -0.69   0.489    -.2683719    .1284423
               prof |  -.0596889   .0914924    -0.65   0.514    -.2390107     .119633
            comform |  -.4518913   .1839851    -2.46   0.014    -.8124955   -.0912871
           econfood |  -.0007801   .0139589    -0.06   0.955    -.0281391     .026579
              house |   .0398331   .0659932     0.60   0.546    -.0895111    .1691773
               oven |  -.0245996    .068776    -0.36   0.721    -.1593981    .1101989
             lchang |  -.1019018   .0979824    -1.04   0.298    -.2939436    .0901401
             llomue |  -.1181573   .0661661    -1.79   0.074    -.2478404    .0115259
            lchuabo |  -.1571635   .0804872    -1.95   0.051    -.3149155    .0005885
           lchitewe |  -.2619135    .178361    -1.47   0.142    -.6114946    .0876676
             lronga |   .0070619   .0783172     0.09   0.928     -.146437    .1605608
            chitsua |    .088878   .1047129     0.85   0.396    -.1163556    .2941116
             living |   .0288468   .0197285     1.46   0.144    -.0098203    .0675138
      _Iinterview_2 |    .087527   .0658988     1.33   0.184    -.0416323    .2166864
      _Iinterview_3 |  -.1114695   .2280648    -0.49   0.625    -.5584682    .3355292
      _Iinterview_4 |  -.1070334   .1989469    -0.54   0.591    -.4969623    .2828954
      _Iinterview_5 |          0  (omitted)
      _Iinterview_6 |  -.1364878   .2459952    -0.55   0.579    -.6186296    .3456539
      _Iinterview_7 |  -.0190219   .1932724    -0.10   0.922    -.3978289    .3597852
      _Iinterview_8 |  -.0579976   .2127858    -0.27   0.785    -.4750501     .359055
      _Iinterview_9 |  -.3202361   .1864745    -1.72   0.086    -.6857195    .0452472
     _Iinterview_10 |  -.0272661   .1040607    -0.26   0.793    -.2312213    .1766891
     _Iinterview_11 |  -.2471048   .1877113    -1.32   0.188    -.6150122    .1208025
     _Iinterview_12 |   .0247794   .1378718     0.18   0.857    -.2454443     .295003
     _Iinterview_13 |  -.3625468   .1321112    -2.74   0.006    -.6214799   -.1036136
     _Iinterview_14 |  -.1557824   .1804494    -0.86   0.388    -.5094568    .1978919
     _Iinterview_15 |    .236216   .1390462     1.70   0.089    -.0363095    .5087415
     _Iinterview_16 |   .0638575   .1526811     0.42   0.676     -.235392     .363107
     _Iinterview_17 |  -.1033795   .1611223    -0.64   0.521    -.4191733    .2124143
     _Iinterview_18 |   .1795533   .1410885     1.27   0.203    -.0969751    .4560816
     _Iinterview_19 |   .0032627   .1479956     0.02   0.982    -.2868033    .2933288
     _Iinterview_20 |          0  (omitted)
     _Iinterview_21 |  -.2141381   .1010952    -2.12   0.034     -.412281   -.0159951
     _Iinterview_22 |          0  (omitted)
     _Iinterview_23 |  -.0306751   .1063824    -0.29   0.773    -.2391807    .1778306
     _Iinterview_24 |  -.0062088   .0586041    -0.11   0.916    -.1210707    .1086532
     _Iinterview_25 |  -.4232274   .0823343    -5.14   0.000    -.5845996   -.2618552
     _Iinterview_26 |  -.1080367   .1009678    -1.07   0.285      -.30593    .0898566
     _Iinterview_27 |   -.032026     .12882    -0.25   0.804    -.2845085    .2204565
     _Iinterview_28 |          0  (omitted)
              _cons |   .8870993   .1085287     8.17   0.000      .674387    1.099812
--------------------+----------------------------------------------------------------
frelimo2_4_3a_lnvar |
              _cons |  -2.052493   .0851219   -24.11   0.000    -2.219329   -1.885657
-------------------------------------------------------------------------------------

 ( 1)  [frelimo2_4_2a_mean]civiceduc - [frelimo2_4_3a_mean]civiceduc = 0

           chi2(  1) =    0.01
         Prob > chi2 =    0.9297
.92966128

 ( 1)  [frelimo2_4_2a_mean]hotline - [frelimo2_4_3a_mean]hotline = 0

           chi2(  1) =    0.82
         Prob > chi2 =    0.3664
.36637075

 ( 1)  [frelimo2_4_2a_mean]verdade - [frelimo2_4_3a_mean]verdade = 0

           chi2(  1) =    1.36
         Prob > chi2 =    0.2437
.24366906

. 
. matrix define means=(m_frelimo2_2_1, m_frelimo2_3_1, m_frelimo2_4_1 \ t_frelimo2_2_1_1, t_frel
> imo2_3_1_1, t_frelimo2_4_1_1 \ t_frelimo2_2_1_2, t_frelimo2_3_1_2, t_frelimo2_4_1_2 \ t_frelim
> o2_2_1_3, t_frelimo2_3_1_3, t_frelimo2_4_1_3 \ t_frelimo2_2_1_4, t_frelimo2_3_1_4, t_frelimo2_
> 4_1_4 \ t_frelimo2_2_5, t_frelimo2_3_5, t_frelimo2_4_5 \ t_frelimo2_2_6, t_frelimo2_3_6, t_fre
> limo2_4_6 \ t_frelimo2_2_7, t_frelimo2_3_7, t_frelimo2_4_7)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_voting.xml") append sheet("voting
>  4") 


note: results saved to outputregs_voting.xml

. xml_tab $list2, save("outputregs_voting.xml") append sheet("voting 4 stats") 


note: results saved to outputregs_voting.xml

. estimates clear

. 
. global list1=""

. global list2=""

. 
. foreach i in $voting5 {
  2. 
.         regress `i' $treat $prov if time==1, cluster(ea)
  3.         estimates store `i'_2_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2_1=r(mean)
  6.         display m_`i'_2_1
  7.         test civiceduc = hotline
  8.         scalar define t_`i'_2_1_1=r(p)
  9.         display t_`i'_2_1_1
 10.         test civiceduc = verdade
 11.         scalar define t_`i'_2_1_2=r(p)
 12.         display t_`i'_2_1_2
 13.         test hotline = verdade
 14.         scalar define t_`i'_2_1_3=r(p)
 15.         display t_`i'_2_1_3
 16.         test civiceduc hotline verdade
 17.         scalar define t_`i'_2_1_4=r(p)
 18.         display t_`i'_2_1_4
 19. 
.         regress `i' $treat $prov if time==1 & lazy==0, cluster(ea)
 20.         estimates store `i'_2_2
 21.         regress `i' $treat $prov if time==1 & lazy==0
 22.         estimates store `i'_2_2a
 23.         
.         regress `i' $treat $prov if time==1 & (lazy==1|control==1), cluster(ea)
 24.         estimates store `i'_2_3
 25.         regress `i' $treat $prov if time==1 & (lazy==1|control==1)
 26.         estimates store `i'_2_3a
 27. 
.         suest `i'_2_2a `i'_2_3a, cluster(ea)
 28.         test [`i'_2_2a_mean]civiceduc=[`i'_2_3a_mean]civiceduc  
 29.         scalar define t_`i'_2_5=r(p)
 30.         display t_`i'_2_5
 31.         test [`i'_2_2a_mean]hotline=[`i'_2_3a_mean]hotline      
 32.         scalar define t_`i'_2_6=r(p)
 33.         display t_`i'_2_6
 34.         test [`i'_2_2a_mean]verdade=[`i'_2_3a_mean]verdade
 35.         scalar define t_`i'_2_7=r(p)
 36.         display t_`i'_2_7
 37. 
.         regress `i' $treat $prov $ea $controls if time==1, cluster(ea)
 38.         estimates store `i'_3_1
 39.         sum `i' if e(sample) & control == 1
 40.         scalar define m_`i'_3_1=r(mean)
 41.         display m_`i'_3_1
 42.         test civiceduc = hotline
 43.         scalar define t_`i'_3_1_1=r(p)
 44.         display t_`i'_3_1_1
 45.         test civiceduc = verdade
 46.         scalar define t_`i'_3_1_2=r(p)
 47.         display t_`i'_3_1_2
 48.         test hotline = verdade
 49.         scalar define t_`i'_3_1_3=r(p)
 50.         display t_`i'_3_1_3
 51.         test civiceduc hotline verdade
 52.         scalar define t_`i'_3_1_4=r(p)
 53.         display t_`i'_3_1_4
 54. 
.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0, cluster(ea)
 55.         estimates store `i'_3_2
 56.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0
 57.         estimates store `i'_3_2a
 58.         
.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1), cluster(ea)
 59.         estimates store `i'_3_3
 60.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1)
 61.         estimates store `i'_3_3a
 62. 
.         suest `i'_3_2a `i'_3_3a, cluster(ea)
 63.         test [`i'_3_2a_mean]civiceduc=[`i'_3_3a_mean]civiceduc  
 64.         scalar define t_`i'_3_5=r(p)
 65.         display t_`i'_3_5
 66.         test [`i'_3_2a_mean]hotline=[`i'_3_3a_mean]hotline      
 67.         scalar define t_`i'_3_6=r(p)
 68.         display t_`i'_3_6
 69.         test [`i'_3_2a_mean]verdade=[`i'_3_3a_mean]verdade
 70.         scalar define t_`i'_3_7=r(p)
 71.         display t_`i'_3_7
 72. 
.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1, cluster(ea)
 73.         estimates store `i'_4_1
 74.         sum `i' if e(sample) & control == 1
 75.         scalar define m_`i'_4_1=r(mean)
 76.         display m_`i'_4_1
 77.         test civiceduc = hotline
 78.         scalar define t_`i'_4_1_1=r(p)
 79.         display t_`i'_4_1_1
 80.         test civiceduc = verdade
 81.         scalar define t_`i'_4_1_2=r(p)
 82.         display t_`i'_4_1_2
 83.         test hotline = verdade
 84.         scalar define t_`i'_4_1_3=r(p)
 85.         display t_`i'_4_1_3
 86.         test civiceduc hotline verdade
 87.         scalar define t_`i'_4_1_4=r(p)
 88.         display t_`i'_4_1_4
 89. 
.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & lazy==0, cluster
> (ea)
 90.         estimates store `i'_4_2
 91.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & lazy==0
 92.         estimates store `i'_4_2a
 93.         
.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & (lazy==1|control
> ==1), cluster(ea)
 94.         estimates store `i'_4_3
 95.         xi: regress `i' $treat $prov $ea $controls i.interviewer if time==1 & (lazy==1|cont
> rol==1)
 96.         estimates store `i'_4_3a
 97. 
.         suest `i'_4_2a `i'_4_3a, cluster(ea)
 98.         test [`i'_4_2a_mean]civiceduc=[`i'_4_3a_mean]civiceduc  
 99.         scalar define t_`i'_4_5=r(p)
100.         display t_`i'_4_5
101.         test [`i'_4_2a_mean]hotline=[`i'_4_3a_mean]hotline      
102.         scalar define t_`i'_4_6=r(p)
103.         display t_`i'_4_6
104.         test [`i'_4_2a_mean]verdade=[`i'_4_3a_mean]verdade
105.         scalar define t_`i'_4_7=r(p)
106.         display t_`i'_4_7
107.         
.         global list1="$list1" + " `i'_2_1" + " `i'_3_1" + " `i'_4_1" + " `i'_2_2" + " `i'_3_2"
>  + " `i'_4_2"  + " `i'_2_3" + " `i'_3_3" + " `i'_4_3"
108.                 
.         }

Linear regression                                      Number of obs =    1048
                                                       F(  6,   160) =    1.92
                                                       Prob > F      =  0.0812
                                                       R-squared     =  0.0127
                                                       Root MSE      =  .12594

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0007219   .0102104    -0.07   0.944    -.0208865    .0194428
     hotline |   .0154112   .0115356     1.34   0.183    -.0073705    .0381929
     verdade |   .0029241     .01024     0.29   0.776    -.0172988    .0231471
         pr1 |    .026463   .0133358     1.98   0.049     .0001261    .0527998
         pr2 |   .0011619   .0093273     0.12   0.901    -.0172587    .0195824
         pr3 |  -.0072727   .0072359    -1.01   0.316     -.021563    .0070176
       _cons |   .0064728   .0095489     0.68   0.499    -.0123853     .025331
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     renamo2 |       252    .0119048    .1086734          0          1
.01190476

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    1.96
            Prob > F =    0.1629
.16293252

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.12
            Prob > F =    0.7293
.72931681

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    1.13
            Prob > F =    0.2893
.28931529

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.78
            Prob > F =    0.5068
.50678708

Linear regression                                      Number of obs =     886
                                                       F(  6,   160) =    1.37
                                                       Prob > F      =  0.2313
                                                       R-squared     =  0.0109
                                                       Root MSE      =  .12452

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.002989   .0088957    -0.34   0.737    -.0205571     .014579
     hotline |   .0129341   .0124593     1.04   0.301    -.0116719      .03754
     verdade |   .0068242   .0116038     0.59   0.557    -.0160922    .0297406
         pr1 |   .0255764   .0130266     1.96   0.051    -.0001498    .0513026
         pr2 |   .0052647   .0100332     0.52   0.600    -.0145499    .0250793
         pr3 |   -.003974   .0076248    -0.52   0.603    -.0190322    .0110843
       _cons |   .0048971   .0096552     0.51   0.613     -.014171    .0239652
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     886
-------------+------------------------------           F(  6,   879) =    1.61
       Model |  .149760928     6  .024960155           Prob > F      =  0.1412
    Residual |  13.6290201   879  .015505142           R-squared     =  0.0109
-------------+------------------------------           Adj R-squared =  0.0041
       Total |   13.778781   885  .015569244           Root MSE      =  .12452

------------------------------------------------------------------------------
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.002989   .0114347    -0.26   0.794    -.0254315    .0194534
     hotline |   .0129341   .0116451     1.11   0.267    -.0099215    .0357896
     verdade |   .0068242   .0118053     0.58   0.563    -.0163457     .029994
         pr1 |   .0255764   .0116553     2.19   0.028     .0027009    .0484519
         pr2 |   .0052647   .0119747     0.44   0.660    -.0182376     .028767
         pr3 |   -.003974   .0118669    -0.33   0.738    -.0272648    .0193168
       _cons |   .0048971    .010721     0.46   0.648    -.0161447    .0259389
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     414
                                                       F(  6,   145) =    1.01
                                                       Prob > F      =  0.4239
                                                       R-squared     =  0.0150
                                                       Root MSE      =  .11963

                                   (Std. Err. adjusted for 146 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0087551   .0201356     0.43   0.664    -.0310422    .0485523
     hotline |   .0233276   .0245699     0.95   0.344    -.0252339     .071889
     verdade |   -.012127   .0073974    -1.64   0.103    -.0267477    .0024937
         pr1 |   .0110877     .02147     0.52   0.606    -.0313468    .0535223
         pr2 |  -.0066338   .0161493    -0.41   0.682    -.0385522    .0252846
         pr3 |  -.0196253   .0130088    -1.51   0.134    -.0453366    .0060861
       _cons |   .0155721   .0133253     1.17   0.244    -.0107649     .041909
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     414
-------------+------------------------------           F(  6,   407) =    1.03
       Model |   .08851547     6  .014752578           Prob > F      =  0.4046
    Residual |  5.82452801   407   .01431088           R-squared     =  0.0150
-------------+------------------------------           Adj R-squared =  0.0004
       Total |  5.91304348   413  .014317297           Root MSE      =  .11963

------------------------------------------------------------------------------
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0087551   .0182459     0.48   0.632    -.0271128     .044623
     hotline |   .0233276   .0173523     1.34   0.180    -.0107837    .0574388
     verdade |   -.012127   .0183888    -0.66   0.510    -.0482759    .0240219
         pr1 |   .0110877   .0165771     0.67   0.504    -.0214997    .0436751
         pr2 |  -.0066338   .0169783    -0.39   0.696      -.04001    .0267424
         pr3 |  -.0196253   .0162578    -1.21   0.228     -.051585    .0123345
       _cons |   .0155721   .0126997     1.23   0.221    -.0093932    .0405373
------------------------------------------------------------------------------

Simultaneous results for renamo2_2_2a, renamo2_2_3a

                                                  Number of obs   =       1048

                                         (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------------
                   |               Robust
                   |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
renamo2_2_2a_mean  |
         civiceduc |   -.002989   .0088654    -0.34   0.736     -.020365    .0143869
           hotline |   .0129341    .012417     1.04   0.298    -.0114028     .037271
           verdade |   .0068242   .0115644     0.59   0.555    -.0158417      .02949
               pr1 |   .0255764   .0129824     1.97   0.049     .0001314    .0510214
               pr2 |   .0052647   .0099991     0.53   0.599    -.0143333    .0248626
               pr3 |   -.003974   .0075989    -0.52   0.601    -.0188676    .0109196
             _cons |   .0048971   .0096224     0.51   0.611    -.0139625    .0237567
-------------------+----------------------------------------------------------------
renamo2_2_2a_lnvar |
             _cons |  -4.166584   .2479294   -16.81   0.000    -4.652516   -3.680651
-------------------+----------------------------------------------------------------
renamo2_2_3a_mean  |
         civiceduc |   .0087551   .0199824     0.44   0.661    -.0304097    .0479199
           hotline |   .0233276    .024383     0.96   0.339    -.0244622    .0711173
           verdade |   -.012127   .0073411    -1.65   0.099    -.0265154    .0022614
               pr1 |   .0110877   .0213066     0.52   0.603    -.0306724    .0528479
               pr2 |  -.0066338   .0160264    -0.41   0.679     -.038045    .0247774
               pr3 |  -.0196253   .0129098    -1.52   0.128     -.044928    .0056775
             _cons |   .0155721   .0132239     1.18   0.239    -.0103463    .0414904
-------------------+----------------------------------------------------------------
renamo2_2_3a_lnvar |
             _cons |  -4.246735   .3846006   -11.04   0.000    -5.000539   -3.492932
------------------------------------------------------------------------------------

 ( 1)  [renamo2_2_2a_mean]civiceduc - [renamo2_2_3a_mean]civiceduc = 0

           chi2(  1) =    0.59
         Prob > chi2 =    0.4435
.44345534

 ( 1)  [renamo2_2_2a_mean]hotline - [renamo2_2_3a_mean]hotline = 0

           chi2(  1) =    0.15
         Prob > chi2 =    0.6952
.69517536

 ( 1)  [renamo2_2_2a_mean]verdade - [renamo2_2_3a_mean]verdade = 0

           chi2(  1) =    4.00
         Prob > chi2 =    0.0456
.04555529

Linear regression                                      Number of obs =    1034
                                                       F( 31,   160) =    0.81
                                                       Prob > F      =  0.7487
                                                       R-squared     =  0.0477
                                                       Root MSE      =  .12236

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0046558   .0123822    -0.38   0.707    -.0291095    .0197978
     hotline |   .0072976   .0115575     0.63   0.529    -.0155273    .0301225
     verdade |  -.0031749   .0114086    -0.28   0.781    -.0257058    .0193559
         pr1 |    .001204   .0200267     0.06   0.952    -.0383467    .0407548
         pr2 |  -.0152565   .0198824    -0.77   0.444    -.0545223    .0240093
         pr3 |  -.0333828   .0225037    -1.48   0.140    -.0778255    .0110599
        post |  -.0187477   .0141203    -1.33   0.186    -.0466339    .0091385
   post_miss |  -.0111607   .0097772    -1.14   0.255    -.0304698    .0081484
      health |   .0029934   .0083002     0.36   0.719    -.0133986    .0193854
 health_miss |  -.0192082   .0123034    -1.56   0.120    -.0435062    .0050898
      police |   .0033846   .0119337     0.28   0.777    -.0201832    .0269524
 police_miss |   .0581593   .0305018     1.91   0.058    -.0020787    .1183972
         sex |   .0161875   .0091976     1.76   0.080    -.0019768    .0343519
         age |  -.0003029   .0003051    -0.99   0.322    -.0009054    .0002996
      single |  -.0085514   .0102288    -0.84   0.404    -.0287522    .0116494
       divor |   .1066672   .1000504     1.07   0.288    -.0909224    .3042568
     norelig |   .0179213   .0242005     0.74   0.460    -.0298724    .0657149
     protest |   .0181691   .0094317     1.93   0.056    -.0004576    .0367957
         com |  -.0006475   .0237258    -0.03   0.978    -.0475036    .0462086
        prof |   .0429779   .0558969     0.77   0.443     -.067413    .1533688
     comform |  -.0167332   .0109121    -1.53   0.127    -.0382836    .0048172
    econfood |   .0036791   .0035831     1.03   0.306    -.0033972    .0107554
       house |    -.02497   .0154364    -1.62   0.108    -.0554553    .0055153
        oven |  -.0047214    .006251    -0.76   0.451    -.0170665    .0076237
      lchang |   .0246088   .0216521     1.14   0.257     -.018152    .0673695
      llomue |   .0190725   .0260241     0.73   0.465    -.0323225    .0704675
     lchuabo |    .037134   .0254033     1.46   0.146    -.0130351    .0873031
    lchitewe |   -.042547   .0167621    -2.54   0.012    -.0756505   -.0094434
      lronga |  -.0178073   .0093055    -1.91   0.057    -.0361847    .0005701
     chitsua |  -.0192697   .0101344    -1.90   0.059    -.0392842    .0007447
      living |  -.0015986     .00413    -0.39   0.699    -.0097549    .0065577
       _cons |   .0311176   .0215077     1.45   0.150    -.0113581    .0735932
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     renamo2 |       250        .012    .1091037          0          1
.012

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    1.15
            Prob > F =    0.2844
.28438515

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.02
            Prob > F =    0.8960
.89601684

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.92
            Prob > F =    0.3381
.33812613

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.47
            Prob > F =    0.7015
.70154405

Linear regression                                      Number of obs =     876
                                                       F( 31,   160) =    0.70
                                                       Prob > F      =  0.8769
                                                       R-squared     =  0.0489
                                                       Root MSE      =   .1246

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0100503   .0104778    -0.96   0.339    -.0307428    .0106423
     hotline |    .007586   .0132172     0.57   0.567    -.0185167    .0336888
     verdade |   .0003213    .012333     0.03   0.979    -.0240351    .0246777
         pr1 |   -.006665   .0252063    -0.26   0.792    -.0564449     .043115
         pr2 |  -.0007778   .0114709    -0.07   0.946    -.0234317    .0218762
         pr3 |  -.0174189   .0139851    -1.25   0.215    -.0450382    .0102003
        post |  -.0188957   .0148036    -1.28   0.204    -.0481313    .0103399
   post_miss |  -.0084931   .0092126    -0.92   0.358     -.026687    .0097009
      health |   .0027868   .0094872     0.29   0.769    -.0159494     .021523
 health_miss |  -.0144472   .0119307    -1.21   0.228    -.0380093    .0091148
      police |  -.0004946   .0121689    -0.04   0.968     -.024527    .0235377
 police_miss |   .0555619   .0382032     1.45   0.148    -.0198857    .1310096
         sex |   .0136306   .0092783     1.47   0.144    -.0046931    .0319543
         age |  -.0002822   .0003412    -0.83   0.409     -.000956    .0003916
      single |  -.0053389   .0114738    -0.47   0.642    -.0279986    .0173208
       divor |   .1329243   .1246155     1.07   0.288     -.113179    .3790276
     norelig |   .0221189   .0281409     0.79   0.433    -.0334566    .0776943
     protest |   .0170073   .0113986     1.49   0.138    -.0055039    .0395185
         com |   .0013638   .0259729     0.05   0.958    -.0499301    .0526577
        prof |  -.0163369   .0104349    -1.57   0.119    -.0369448     .004271
     comform |  -.0109755   .0096449    -1.14   0.257    -.0300232    .0080723
    econfood |   .0035972   .0039294     0.92   0.361     -.004163    .0113574
       house |  -.0225618   .0171822    -1.31   0.191    -.0564949    .0113713
        oven |  -.0080376   .0069277    -1.16   0.248    -.0217191    .0056439
      lchang |   .0078303   .0113315     0.69   0.491    -.0145483    .0302089
      llomue |   .0091534   .0260648     0.35   0.726    -.0423219    .0606287
     lchuabo |   .0508263    .030727     1.65   0.100    -.0098564     .111509
    lchitewe |  -.0420595   .0212453    -1.98   0.049    -.0840168   -.0001022
      lronga |  -.0176664   .0101812    -1.74   0.085    -.0377732    .0024404
     chitsua |  -.0187616   .0100321    -1.87   0.063    -.0385741    .0010509
      living |  -.0029512   .0045249    -0.65   0.515    -.0118874     .005985
       _cons |   .0378814   .0239781     1.58   0.116     -.009473    .0852358
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     876
-------------+------------------------------           F( 31,   844) =    1.40
       Model |  .673388958    31  .021722224           Prob > F      =  0.0740
    Residual |  13.1028667   844  .015524724           R-squared     =  0.0489
-------------+------------------------------           Adj R-squared =  0.0139
       Total |  13.7762557   875  .015744292           Root MSE      =   .1246

------------------------------------------------------------------------------
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0100503   .0122644    -0.82   0.413    -.0341226    .0140221
     hotline |    .007586   .0121474     0.62   0.532    -.0162566    .0314287
     verdade |   .0003213   .0126268     0.03   0.980    -.0244624     .025105
         pr1 |   -.006665   .0189819    -0.35   0.726    -.0439223    .0305923
         pr2 |  -.0007778   .0252506    -0.03   0.975    -.0503391    .0487836
         pr3 |  -.0174189   .0260103    -0.67   0.503    -.0684715    .0336336
        post |  -.0188957   .0175108    -1.08   0.281    -.0532655    .0154742
   post_miss |  -.0084931   .0282754    -0.30   0.764    -.0639915    .0470053
      health |   .0027868   .0104677     0.27   0.790     -.017759    .0233326
 health_miss |  -.0144472   .0341886    -0.42   0.673     -.081552    .0526575
      police |  -.0004946   .0129511    -0.04   0.970    -.0259147    .0249254
 police_miss |   .0555619   .0533222     1.04   0.298    -.0490978    .1602217
         sex |   .0136306   .0090959     1.50   0.134    -.0042226    .0314838
         age |  -.0002822   .0003529    -0.80   0.424    -.0009749    .0004106
      single |  -.0053389    .011885    -0.45   0.653    -.0286665    .0179888
       divor |   .1329243   .0482277     2.76   0.006     .0382639    .2275847
     norelig |   .0221189   .0223169     0.99   0.322    -.0216842     .065922
     protest |   .0170073   .0107499     1.58   0.114    -.0040924     .038107
         com |   .0013638    .020129     0.07   0.946    -.0381449    .0408725
        prof |  -.0163369    .035677    -0.46   0.647    -.0863631    .0536893
     comform |  -.0109755   .0405496    -0.27   0.787    -.0905654    .0686145
    econfood |   .0035972   .0037909     0.95   0.343    -.0038435    .0110379
       house |  -.0225618   .0127303    -1.77   0.077    -.0475485    .0024249
        oven |  -.0080376   .0166923    -0.48   0.630    -.0408009    .0247257
      lchang |   .0078303   .0223474     0.35   0.726    -.0360327    .0516934
      llomue |   .0091534    .018511     0.49   0.621    -.0271796    .0454864
     lchuabo |   .0508263   .0175136     2.90   0.004     .0164509    .0852016
    lchitewe |  -.0420595   .0488035    -0.86   0.389      -.13785     .053731
      lronga |  -.0176664    .017948    -0.98   0.325    -.0528943    .0175615
     chitsua |  -.0187616   .0413919    -0.45   0.650    -.1000048    .0624815
      living |  -.0029512   .0042618    -0.69   0.489    -.0113161    .0054137
       _cons |   .0378814   .0230355     1.64   0.100    -.0073323    .0830951
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     408
                                                       F( 31,   143) =    0.19
                                                       Prob > F      =  1.0000
                                                       R-squared     =  0.0808
                                                       Root MSE      =  .10988

                                   (Std. Err. adjusted for 144 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0016293   .0268785     0.06   0.952    -.0515012    .0547599
     hotline |  -.0000922    .013188    -0.01   0.994    -.0261607    .0259764
     verdade |  -.0195287   .0143115    -1.36   0.175    -.0478181    .0087606
         pr1 |   -.012283   .0347718    -0.35   0.724    -.0810161    .0564501
         pr2 |  -.0356969   .0475837    -0.75   0.454    -.1297552    .0583615
         pr3 |  -.0539427   .0467588    -1.15   0.251    -.1463705     .038485
        post |  -.0339594   .0281823    -1.20   0.230    -.0896671    .0217484
   post_miss |   -.015118   .0139967    -1.08   0.282    -.0427851     .012549
      health |  -.0033091   .0161895    -0.20   0.838    -.0353107    .0286924
 health_miss |  -.0071039   .0275601    -0.26   0.797    -.0615817    .0473739
      police |   .0272309    .027723     0.98   0.328     -.027569    .0820307
 police_miss |  -.0157829   .0284865    -0.55   0.580    -.0720918    .0405261
         sex |    .017341   .0124168     1.40   0.165    -.0072032    .0418853
         age |   -.000426   .0003369    -1.26   0.208     -.001092    .0002399
      single |  -.0242262   .0114638    -2.11   0.036    -.0468866   -.0015658
       divor |  -.0101547   .0138939    -0.73   0.466    -.0376187    .0173093
     norelig |  -.0078807     .01534    -0.51   0.608    -.0382032    .0224418
     protest |  -.0016841   .0127724    -0.13   0.895    -.0269312     .023563
         com |  -.0217258   .0133622    -1.63   0.106    -.0481388    .0046873
        prof |   .1022622   .0948545     1.08   0.283    -.0852359    .2897603
     comform |   -.005492   .0197723    -0.28   0.782    -.0445756    .0335917
    econfood |   .0021867   .0043006     0.51   0.612    -.0063143    .0106876
       house |  -.0241246   .0174211    -1.38   0.168    -.0585609    .0103116
        oven |  -.0018267   .0068697    -0.27   0.791     -.015406    .0117526
      lchang |   .0590849   .0468729     1.26   0.210    -.0335684    .1517382
      llomue |   .0282068   .0361157     0.78   0.436    -.0431828    .0995964
     lchuabo |   .0446473   .0491296     0.91   0.365    -.0524668    .1417614
    lchitewe |  -.0089662   .0291949    -0.31   0.759    -.0666756    .0487432
      lronga |  -.0220276   .0157462    -1.40   0.164     -.053153    .0090977
     chitsua |  -.0168326   .0279136    -0.60   0.547    -.0720093     .038344
      living |   .0004003   .0055041     0.07   0.942    -.0104796    .0112801
       _cons |   .0362955   .0330316     1.10   0.274    -.0289979    .1015889
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     408
-------------+------------------------------           F( 31,   376) =    1.07
       Model |  .398890293    31  .012867429           Prob > F      =  0.3756
    Residual |   4.5398352   376   .01207403           R-squared     =  0.0808
-------------+------------------------------           Adj R-squared =  0.0050
       Total |  4.93872549   407  .012134461           Root MSE      =  .10988

------------------------------------------------------------------------------
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0016293   .0182714     0.09   0.929    -.0342976    .0375563
     hotline |  -.0000922   .0169979    -0.01   0.996    -.0335151    .0333307
     verdade |  -.0195287    .018438    -1.06   0.290    -.0557832    .0167258
         pr1 |   -.012283   .0266006    -0.46   0.645    -.0645876    .0400215
         pr2 |  -.0356969   .0333525    -1.07   0.285    -.1012777     .029884
         pr3 |  -.0539427   .0341374    -1.58   0.115    -.1210668    .0131813
        post |  -.0339594    .020327    -1.67   0.096    -.0739283    .0060095
   post_miss |   -.015118   .0290503    -0.52   0.603    -.0722395    .0420034
      health |  -.0033091   .0144744    -0.23   0.819      -.03177    .0251517
 health_miss |  -.0071039   .0526526    -0.13   0.893    -.1106344    .0964266
      police |   .0272309   .0170678     1.60   0.111    -.0063294    .0607911
 police_miss |  -.0157829   .0784519    -0.20   0.841    -.1700424    .1384766
         sex |    .017341   .0118146     1.47   0.143    -.0058898    .0405719
         age |   -.000426    .000489    -0.87   0.384    -.0013875    .0005354
      single |  -.0242262   .0148594    -1.63   0.104    -.0534441    .0049917
       divor |  -.0101547   .0650146    -0.16   0.876    -.1379924     .117683
     norelig |  -.0078807   .0338344    -0.23   0.816     -.074409    .0586476
     protest |  -.0016841   .0151257    -0.11   0.911    -.0314256    .0280575
         com |  -.0217258    .031144    -0.70   0.486     -.082964    .0395125
        prof |   .1022622   .0412933     2.48   0.014     .0210674     .183457
     comform |   -.005492   .0462611    -0.12   0.906    -.0964548    .0854709
    econfood |   .0021867   .0051855     0.42   0.673    -.0080095    .0123829
       house |  -.0241246   .0165655    -1.46   0.146    -.0566973    .0084481
        oven |  -.0018267   .0228193    -0.08   0.936    -.0466961    .0430427
      lchang |   .0590849   .0294032     2.01   0.045     .0012695    .1169003
      llomue |   .0282068   .0262833     1.07   0.284    -.0234738    .0798874
     lchuabo |   .0446473   .0248199     1.80   0.073    -.0041559    .0934505
    lchitewe |  -.0089662   .0532252    -0.17   0.866    -.1136226    .0956902
      lronga |  -.0220276   .0224399    -0.98   0.327     -.066151    .0220957
     chitsua |  -.0168326   .0581807    -0.29   0.772    -.1312329    .0975676
      living |   .0004003   .0054764     0.07   0.942    -.0103679    .0111685
       _cons |   .0362955   .0291502     1.25   0.214    -.0210223    .0936133
------------------------------------------------------------------------------

Simultaneous results for renamo2_3_2a, renamo2_3_3a

                                                  Number of obs   =       1034

                                         (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------------
                   |               Robust
                   |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
renamo2_3_2a_mean  |
         civiceduc |  -.0100503   .0102905    -0.98   0.329    -.0302193    .0101188
           hotline |    .007586    .012981     0.58   0.559    -.0178562    .0330283
           verdade |   .0003213   .0121125     0.03   0.979    -.0234188    .0240614
               pr1 |   -.006665   .0247558    -0.27   0.788    -.0551854    .0418554
               pr2 |  -.0007778   .0112659    -0.07   0.945    -.0228585     .021303
               pr3 |  -.0174189   .0137352    -1.27   0.205    -.0443394    .0095015
              post |  -.0188957    .014539    -1.30   0.194    -.0473915    .0096002
         post_miss |  -.0084931   .0090479    -0.94   0.348    -.0262267    .0092405
            health |   .0027868   .0093176     0.30   0.765    -.0154753    .0210489
       health_miss |  -.0144472   .0117175    -1.23   0.218    -.0374131    .0085186
            police |  -.0004946   .0119514    -0.04   0.967    -.0239189    .0229296
       police_miss |   .0555619   .0375204     1.48   0.139    -.0179767    .1291006
               sex |   .0136306   .0091124     1.50   0.135    -.0042295    .0314907
               age |  -.0002822   .0003351    -0.84   0.400     -.000939    .0003746
            single |  -.0053389   .0112688    -0.47   0.636    -.0274252    .0167475
             divor |   .1329243   .1223881     1.09   0.277    -.1069519    .3728005
           norelig |   .0221189   .0276379     0.80   0.424    -.0320504    .0762881
           protest |   .0170073   .0111949     1.52   0.129    -.0049343    .0389489
               com |   .0013638   .0255087     0.05   0.957    -.0486323    .0513599
              prof |  -.0163369   .0102484    -1.59   0.111    -.0364234    .0037496
           comform |  -.0109755   .0094725    -1.16   0.247    -.0295413    .0075903
          econfood |   .0035972   .0038592     0.93   0.351    -.0039666     .011161
             house |  -.0225618   .0168751    -1.34   0.181    -.0556363    .0105127
              oven |  -.0080376   .0068039    -1.18   0.237    -.0213729    .0052977
            lchang |   .0078303    .011129     0.70   0.482     -.013982    .0296427
            llomue |   .0091534   .0255989     0.36   0.721    -.0410195    .0593263
           lchuabo |   .0508263   .0301777     1.68   0.092     -.008321    .1099736
          lchitewe |  -.0420595   .0208655    -2.02   0.044    -.0829552   -.0011639
            lronga |  -.0176664   .0099992    -1.77   0.077    -.0372645    .0019316
           chitsua |  -.0187616   .0098528    -1.90   0.057    -.0380728    .0005495
            living |  -.0029512    .004444    -0.66   0.507    -.0116613    .0057589
             _cons |   .0378814   .0235495     1.61   0.108    -.0082748    .0840376
-------------------+----------------------------------------------------------------
renamo2_3_2a_lnvar |
             _cons |  -4.165321    .234396   -17.77   0.000    -4.624729   -3.705914
-------------------+----------------------------------------------------------------
renamo2_3_3a_mean  |
         civiceduc |   .0016293   .0258251     0.06   0.950    -.0489869    .0522456
           hotline |  -.0000922   .0126711    -0.01   0.994    -.0249271    .0247427
           verdade |  -.0195287   .0137506    -1.42   0.156    -.0464793    .0074219
               pr1 |   -.012283    .033409    -0.37   0.713    -.0777634    .0531974
               pr2 |  -.0356969   .0457188    -0.78   0.435     -.125304    .0539103
               pr3 |  -.0539427   .0449262    -1.20   0.230    -.1419965     .034111
              post |  -.0339594   .0270778    -1.25   0.210    -.0870308    .0191121
         post_miss |   -.015118   .0134481    -1.12   0.261    -.0414758    .0112397
            health |  -.0033091   .0155549    -0.21   0.832    -.0337963     .027178
       health_miss |  -.0071039   .0264799    -0.27   0.788    -.0590036    .0447958
            police |   .0272309   .0266365     1.02   0.307    -.0249757    .0794374
       police_miss |  -.0157829     .02737    -0.58   0.564    -.0694271    .0378614
               sex |    .017341   .0119302     1.45   0.146    -.0060417    .0407238
               age |   -.000426   .0003237    -1.32   0.188    -.0010604    .0002084
            single |  -.0242262   .0110145    -2.20   0.028    -.0458142   -.0026381
             divor |  -.0101547   .0133494    -0.76   0.447     -.036319    .0160096
           norelig |  -.0078807   .0147388    -0.53   0.593    -.0367683    .0210068
           protest |  -.0016841   .0122718    -0.14   0.891    -.0257364    .0223683
               com |  -.0217258   .0128385    -1.69   0.091    -.0468888    .0034373
              prof |   .1022622   .0911369     1.12   0.262    -.0763629    .2808873
           comform |   -.005492   .0189973    -0.29   0.773    -.0427261    .0317422
          econfood |   .0021867    .004132     0.53   0.597     -.005912    .0102854
             house |  -.0241246   .0167384    -1.44   0.150    -.0569312    .0086819
              oven |  -.0018267   .0066005    -0.28   0.782    -.0147633      .01111
            lchang |   .0590849   .0450358     1.31   0.190    -.0291838    .1473535
            llomue |   .0282068   .0347002     0.81   0.416    -.0398044     .096218
           lchuabo |   .0446473   .0472041     0.95   0.344     -.047871    .1371656
          lchitewe |  -.0089662   .0280507    -0.32   0.749    -.0639446    .0460122
            lronga |  -.0220276    .015129    -1.46   0.145      -.05168    .0076248
           chitsua |  -.0168326   .0268196    -0.63   0.530    -.0693981    .0357329
            living |   .0004003   .0052884     0.08   0.940    -.0099647    .0107653
             _cons |   .0362955   .0317371     1.14   0.253     -.025908     .098499
-------------------+----------------------------------------------------------------
renamo2_3_3a_lnvar |
             _cons |  -4.416698   .3783289   -11.67   0.000    -5.158209   -3.675187
------------------------------------------------------------------------------------

 ( 1)  [renamo2_3_2a_mean]civiceduc - [renamo2_3_3a_mean]civiceduc = 0

           chi2(  1) =    0.34
         Prob > chi2 =    0.5612
.56117632

 ( 1)  [renamo2_3_2a_mean]hotline - [renamo2_3_3a_mean]hotline = 0

           chi2(  1) =    0.25
         Prob > chi2 =    0.6137
.61373753

 ( 1)  [renamo2_3_2a_mean]verdade - [renamo2_3_3a_mean]verdade = 0

           chi2(  1) =    3.24
         Prob > chi2 =    0.0717
.07169123
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_22 omitted because of collinearity

Linear regression                                      Number of obs =    1031
                                                       F( 54,   160) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.0652
                                                       Root MSE      =  .12302

                                     (Std. Err. adjusted for 161 clusters in ea)
--------------------------------------------------------------------------------
               |               Robust
       renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |  -.0051511   .0128209    -0.40   0.688    -.0304712     .020169
       hotline |   .0075897   .0120973     0.63   0.531    -.0163012    .0314807
       verdade |  -.0029984   .0112335    -0.27   0.790    -.0251833    .0191866
           pr1 |   .0005261   .0376331     0.01   0.989    -.0737957    .0748478
           pr2 |  -.0366216   .0192025    -1.91   0.058    -.0745447    .0013016
           pr3 |  -.0421864   .0288877    -1.46   0.146    -.0992368    .0148639
          post |   -.019322   .0143215    -1.35   0.179    -.0476056    .0089616
     post_miss |  -.0097491   .0099721    -0.98   0.330     -.029443    .0099449
        health |   .0061291    .008737     0.70   0.484    -.0111255    .0233838
   health_miss |  -.0173118   .0138596    -1.25   0.213    -.0446832    .0100596
        police |   .0029109   .0117126     0.25   0.804    -.0202204    .0260422
   police_miss |   .0566095   .0319317     1.77   0.078    -.0064525    .1196715
           sex |    .015853   .0094258     1.68   0.095    -.0027621    .0344681
           age |  -.0002788   .0003055    -0.91   0.363    -.0008821    .0003245
        single |  -.0077082     .01182    -0.65   0.515    -.0310515     .015635
         divor |   .1120442   .1010247     1.11   0.269    -.0874696     .311558
       norelig |   .0174389   .0235569     0.74   0.460    -.0290837    .0639614
       protest |    .019467   .0102138     1.91   0.058    -.0007042    .0396382
           com |  -.0049237   .0248244    -0.20   0.843    -.0539494    .0441019
          prof |   .0437837   .0555352     0.79   0.432     -.065893    .1534603
       comform |  -.0213376   .0188514    -1.13   0.259    -.0585672     .015892
      econfood |   .0026528   .0039078     0.68   0.498    -.0050646    .0103703
         house |  -.0260119   .0178596    -1.46   0.147    -.0612828     .009259
          oven |  -.0071178   .0075806    -0.94   0.349    -.0220887    .0078531
        lchang |   .0220778   .0210113     1.05   0.295    -.0194174    .0635731
        llomue |   .0186697   .0264366     0.71   0.481    -.0335401    .0708794
       lchuabo |   .0358499   .0256999     1.39   0.165    -.0149049    .0866048
      lchitewe |  -.0454251   .0202191    -2.25   0.026    -.0853558   -.0054943
        lronga |  -.0236033   .0126593    -1.86   0.064    -.0486041    .0013976
       chitsua |  -.0235411   .0126452    -1.86   0.064    -.0485141    .0014318
        living |  -.0028253   .0042278    -0.67   0.505    -.0111747    .0055241
 _Iinterview_2 |  -.0005293   .0100059    -0.05   0.958    -.0202899    .0192313
 _Iinterview_3 |   .0146001     .02185     0.67   0.505    -.0285515    .0577517
 _Iinterview_4 |   .0135714   .0180069     0.75   0.452    -.0219905    .0491332
 _Iinterview_5 |   .0130034   .0214923     0.61   0.546    -.0294418    .0554486
 _Iinterview_6 |   .0001282   .0230849     0.01   0.996    -.0454622    .0457185
 _Iinterview_7 |   .0323944   .0176154     1.84   0.068    -.0023944    .0671832
 _Iinterview_8 |   .0195328   .0199523     0.98   0.329    -.0198709    .0589365
 _Iinterview_9 |    .023053   .0117575     1.96   0.052     -.000167    .0462729
_Iinterview_10 |   .0359808   .0158197     2.27   0.024     .0047385    .0672232
_Iinterview_11 |   .0442707   .0223661     1.98   0.049     .0000998    .0884416
_Iinterview_12 |   .0292009   .0293893     0.99   0.322    -.0288401    .0872419
_Iinterview_13 |   .0174477   .0122832     1.42   0.157    -.0068105    .0417059
_Iinterview_14 |   .0518832   .0304494     1.70   0.090    -.0082513    .1120177
_Iinterview_15 |  -.0283475   .0345508    -0.82   0.413     -.096582    .0398871
_Iinterview_16 |   .0254304    .046646     0.55   0.586     -.066691    .1175517
_Iinterview_17 |    .027031   .0452355     0.60   0.551    -.0623047    .1163667
_Iinterview_18 |   -.029843   .0342343    -0.87   0.385    -.0974524    .0377664
_Iinterview_19 |   .0225092   .0389776     0.58   0.564    -.0544678    .0994861
_Iinterview_20 |   .0260868   .0283782     0.92   0.359    -.0299573    .0821309
_Iinterview_21 |   .0022507   .0085102     0.26   0.792    -.0145561    .0190575
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |   .0191998   .0236887     0.81   0.419    -.0275831    .0659827
_Iinterview_24 |  -.0041573   .0055062    -0.76   0.451    -.0150314    .0067168
_Iinterview_25 |   .0089254   .0149822     0.60   0.552    -.0206629    .0385138
_Iinterview_26 |   .0485629   .0493693     0.98   0.327    -.0489366    .1460624
_Iinterview_27 |  -.0012553   .0069068    -0.18   0.856    -.0148955    .0123849
_Iinterview_28 |  -.0597128   .0465867    -1.28   0.202     -.151717    .0322914
         _cons |   .0276686   .0261294     1.06   0.291    -.0239345    .0792716
--------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     renamo2 |       250        .012    .1091037          0          1
.012

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    1.27
            Prob > F =    0.2622
.26217194

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.03
            Prob > F =    0.8534
.85342883

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.83
            Prob > F =    0.3626
.36262565

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    0.48
            Prob > F =    0.6960
.69604854
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_22 omitted because of collinearity

Linear regression                                      Number of obs =     874
                                                       F( 53,   160) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.0709
                                                       Root MSE      =  .12524

                                     (Std. Err. adjusted for 161 clusters in ea)
--------------------------------------------------------------------------------
               |               Robust
       renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |  -.0114102   .0113053    -1.01   0.314    -.0337372    .0109167
       hotline |   .0072279   .0136394     0.53   0.597    -.0197086    .0341644
       verdade |  -.0002758   .0120363    -0.02   0.982    -.0240463    .0234947
           pr1 |  -.0217675   .0421713    -0.52   0.606    -.1050516    .0615166
           pr2 |  -.0240037   .0227981    -1.05   0.294    -.0690278    .0210204
           pr3 |  -.0303244   .0296099    -1.02   0.307    -.0888009    .0281522
          post |  -.0185921   .0144997    -1.28   0.202    -.0472276    .0100433
     post_miss |  -.0080806   .0098456    -0.82   0.413    -.0275247    .0113636
        health |   .0053072   .0101501     0.52   0.602    -.0147382    .0253526
   health_miss |  -.0106899   .0136441    -0.78   0.435    -.0376356    .0162559
        police |  -.0008489    .011605    -0.07   0.942    -.0237677      .02207
   police_miss |   .0558421   .0417672     1.34   0.183    -.0266441    .1383283
           sex |   .0123972   .0092132     1.35   0.180    -.0057978    .0305923
           age |  -.0002569   .0003494    -0.74   0.463     -.000947    .0004331
        single |  -.0036914   .0126351    -0.29   0.771    -.0286445    .0212617
         divor |   .1477205   .1245627     1.19   0.237    -.0982787    .3937197
       norelig |   .0239184   .0274601     0.87   0.385    -.0303125    .0781493
       protest |   .0187003   .0118345     1.58   0.116    -.0046717    .0420722
           com |  -.0022314   .0270781    -0.08   0.934    -.0557079    .0512452
          prof |  -.0151944   .0135434    -1.12   0.264    -.0419412    .0115525
       comform |  -.0206705   .0211261    -0.98   0.329    -.0623925    .0210515
      econfood |   .0025323   .0042954     0.59   0.556    -.0059508    .0110153
         house |   -.018052   .0180058    -1.00   0.318    -.0536118    .0175078
          oven |  -.0086926   .0081305    -1.07   0.287    -.0247495    .0073644
        lchang |   .0030502   .0086346     0.35   0.724    -.0140023    .0201027
        llomue |   .0101373   .0254844     0.40   0.691    -.0401919    .0604664
       lchuabo |   .0501336    .031159     1.61   0.110    -.0114023    .1116695
      lchitewe |  -.0458305   .0220443    -2.08   0.039    -.0893659   -.0022952
        lronga |  -.0256778   .0144973    -1.77   0.078    -.0543086     .002953
       chitsua |  -.0215228   .0126527    -1.70   0.091    -.0465106    .0034651
        living |  -.0042157   .0046892    -0.90   0.370    -.0134765     .005045
 _Iinterview_2 |    .006846   .0120248     0.57   0.570    -.0169017    .0305937
 _Iinterview_3 |   .0215832   .0285473     0.76   0.451     -.034795    .0779613
 _Iinterview_4 |   .0191128   .0238577     0.80   0.424    -.0280038    .0662293
 _Iinterview_5 |   .0147193    .028353     0.52   0.604    -.0412751    .0707138
 _Iinterview_6 |   .0128521   .0289784     0.44   0.658    -.0443773    .0700815
 _Iinterview_7 |   .0406619   .0240712     1.69   0.093    -.0068762    .0882001
 _Iinterview_8 |   .0259414   .0279029     0.93   0.354    -.0291641    .0810468
 _Iinterview_9 |   .0234801   .0193316     1.21   0.226     -.014698    .0616582
_Iinterview_10 |   .0337115   .0268044     1.26   0.210    -.0192246    .0866476
_Iinterview_11 |   .0474653   .0322884     1.47   0.144    -.0163011    .1112316
_Iinterview_12 |   .0471697   .0385927     1.22   0.223     -.029047    .1233864
_Iinterview_13 |   .0158858   .0208903     0.76   0.448    -.0253704    .0571421
_Iinterview_14 |   .0629366   .0396509     1.59   0.114      -.01537    .1412433
_Iinterview_15 |  -.0052138   .0339885    -0.15   0.878    -.0723377      .06191
_Iinterview_16 |   .0230213   .0400166     0.58   0.566    -.0560075    .1020501
_Iinterview_17 |   .0529558   .0464798     1.14   0.256    -.0388371    .1447487
_Iinterview_18 |  -.0145606   .0321592    -0.45   0.651    -.0780719    .0489506
_Iinterview_19 |   .0499489   .0469774     1.06   0.289    -.0428269    .1427247
_Iinterview_20 |   .0205438   .0310589     0.66   0.509    -.0407944     .081882
_Iinterview_21 |   .0001971   .0078087     0.03   0.980    -.0152242    .0156185
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |    .022073   .0275754     0.80   0.425    -.0323858    .0765317
_Iinterview_24 |   -.002175   .0057847    -0.38   0.707    -.0135992    .0092492
_Iinterview_25 |   .0063478   .0152746     0.42   0.678     -.023818    .0365136
_Iinterview_26 |   .0592533   .0555792     1.07   0.288    -.0505101    .1690167
_Iinterview_27 |  -.0026956   .0083294    -0.32   0.747    -.0191453    .0137542
_Iinterview_28 |  -.0522012    .042459    -1.23   0.221    -.1360536    .0316511
         _cons |   .0289512   .0283856     1.02   0.309    -.0271076    .0850101
--------------------------------------------------------------------------------
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_22 omitted because of collinearity

      Source |       SS       df       MS              Number of obs =     874
-------------+------------------------------           F( 57,   816) =    1.09
       Model |  .976523636    57  .017131994           Prob > F      =  0.3026
    Residual |  12.7992201   816  .015685319           R-squared     =  0.0709
-------------+------------------------------           Adj R-squared =  0.0060
       Total |  13.7757437   873  .015779775           Root MSE      =  .12524

--------------------------------------------------------------------------------
       renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |  -.0114102   .0125461    -0.91   0.363    -.0360368    .0132163
       hotline |   .0072279   .0124017     0.58   0.560    -.0171151    .0315708
       verdade |  -.0002758   .0128765    -0.02   0.983    -.0255507    .0249992
           pr1 |  -.0217675   .0925609    -0.24   0.814    -.2034529     .159918
           pr2 |  -.0240037   .1315125    -0.18   0.855    -.2821463    .2341389
           pr3 |  -.0303244   .1459295    -0.21   0.835    -.3167659    .2561171
          post |  -.0185921    .017986    -1.03   0.302    -.0538963    .0167121
     post_miss |  -.0080806   .0287695    -0.28   0.779    -.0645515    .0483903
        health |   .0053072   .0106908     0.50   0.620    -.0156776     .026292
   health_miss |  -.0106899   .0348607    -0.31   0.759    -.0791171    .0577373
        police |  -.0008489   .0133556    -0.06   0.949    -.0270642    .0253665
   police_miss |   .0558421   .0542083     1.03   0.303     -.050562    .1622461
           sex |   .0123972   .0092774     1.34   0.182    -.0058132    .0306076
           age |  -.0002569    .000361    -0.71   0.477    -.0009655    .0004516
        single |  -.0036914   .0126661    -0.29   0.771    -.0285533    .0211705
         divor |   .1477205   .0489042     3.02   0.003     .0517277    .2437133
       norelig |   .0239184   .0226372     1.06   0.291    -.0205157    .0683524
       protest |   .0187003    .011051     1.69   0.091    -.0029915     .040392
           com |  -.0022314   .0206976    -0.11   0.914    -.0428582    .0383955
          prof |  -.0151944   .0365088    -0.42   0.677    -.0868567     .056468
       comform |  -.0206705   .0419243    -0.49   0.622    -.1029627    .0616217
      econfood |   .0025323    .004075     0.62   0.535    -.0054665     .010531
         house |   -.018052   .0145107    -1.24   0.214    -.0465348    .0104308
          oven |  -.0086926   .0174211    -0.50   0.618     -.042888    .0255028
        lchang |   .0030502   .0233193     0.13   0.896    -.0427227    .0488232
        llomue |   .0101373   .0190474     0.53   0.595    -.0272505     .047525
       lchuabo |   .0501336   .0178699     2.81   0.005     .0150571      .08521
      lchitewe |  -.0458305   .0498706    -0.92   0.358    -.1437204    .0520593
        lronga |  -.0256778   .0190071    -1.35   0.177    -.0629863    .0116307
       chitsua |  -.0215228   .0421192    -0.51   0.609    -.1041975    .0611519
        living |  -.0042157   .0044073    -0.96   0.339    -.0128666    .0044352
 _Iinterview_2 |    .006846   .0596537     0.11   0.909    -.1102468    .1239387
 _Iinterview_3 |   .0215832   .1452188     0.15   0.882    -.2634632    .3066296
 _Iinterview_4 |   .0191128   .1435585     0.13   0.894    -.2626747    .3009002
 _Iinterview_5 |   .0147193   .1913108     0.08   0.939    -.3607999    .3902386
 _Iinterview_6 |   .0128521   .1463296     0.09   0.930    -.2743747    .3000789
 _Iinterview_7 |   .0406619   .1441293     0.28   0.778    -.2422459    .3235698
 _Iinterview_8 |   .0259414   .1455883     0.18   0.859    -.2598302     .311713
 _Iinterview_9 |   .0234801   .1320971     0.18   0.859      -.23581    .2827702
_Iinterview_10 |   .0337115   .1412886     0.24   0.811    -.2436204    .3110434
_Iinterview_11 |   .0474653   .1323522     0.36   0.720    -.2123256    .3072562
_Iinterview_12 |   .0471697    .133432     0.35   0.724    -.2147407    .3090801
_Iinterview_13 |   .0158858   .1323365     0.12   0.904    -.2438743    .2756459
_Iinterview_14 |   .0629366   .1330329     0.47   0.636    -.1981904    .3240637
_Iinterview_15 |  -.0052138   .0985652    -0.05   0.958    -.1986849    .1882573
_Iinterview_16 |   .0230213   .0960646     0.24   0.811    -.1655415    .2115841
_Iinterview_17 |   .0529558   .0951481     0.56   0.578    -.1338081    .2397197
_Iinterview_18 |  -.0145606   .0949835    -0.15   0.878    -.2010014    .1718801
_Iinterview_19 |   .0499489   .0951137     0.53   0.600    -.1367475    .2366453
_Iinterview_20 |   .0205438    .093213     0.22   0.826    -.1624218    .2035093
_Iinterview_21 |   .0001971   .0282602     0.01   0.994    -.0552742    .0556684
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |    .022073   .0273147     0.81   0.419    -.0315424    .0756883
_Iinterview_24 |   -.002175   .0265562    -0.08   0.935    -.0543016    .0499516
_Iinterview_25 |   .0063478    .060467     0.10   0.916    -.1123414    .1250371
_Iinterview_26 |   .0592533   .0358285     1.65   0.099    -.0110736    .1295802
_Iinterview_27 |  -.0026956   .0324714    -0.08   0.934    -.0664329    .0610417
_Iinterview_28 |  -.0522012   .1577589    -0.33   0.741    -.3618623    .2574598
         _cons |   .0289512   .0295297     0.98   0.327    -.0290119    .0869143
--------------------------------------------------------------------------------
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_5 omitted because of collinearity
note: _Iinterview_20 omitted because of collinearity
note: _Iinterview_22 omitted because of collinearity
note: _Iinterview_28 omitted because of collinearity

Linear regression                                      Number of obs =     407
                                                       F( 51,   143) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.1162
                                                       Root MSE      =  .11135

                                     (Std. Err. adjusted for 144 clusters in ea)
--------------------------------------------------------------------------------
               |               Robust
       renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |      .0003   .0251747     0.01   0.991    -.0494627    .0500627
       hotline |  -.0006795    .014968    -0.05   0.964    -.0302666    .0289076
       verdade |  -.0144449   .0135463    -1.07   0.288    -.0412218     .012332
           pr1 |   .0558411   .0731409     0.76   0.446    -.0887359    .2004181
           pr2 |  -.0263313   .0771038    -0.34   0.733    -.1787417    .1260791
           pr3 |  -.0192045   .0868574    -0.22   0.825    -.1908949    .1524859
          post |   -.034257   .0271782    -1.26   0.210    -.0879799     .019466
     post_miss |  -.0134409   .0121977    -1.10   0.272    -.0375521    .0106702
        health |  -.0008101   .0194511    -0.04   0.967     -.039259    .0376388
   health_miss |  -.0140997   .0357577    -0.39   0.694    -.0847817    .0565823
        police |   .0269369   .0258307     1.04   0.299    -.0241224    .0779963
   police_miss |  -.0191058    .043028    -0.44   0.658    -.1041589    .0659474
           sex |   .0179295   .0133127     1.35   0.180    -.0083857    .0442446
           age |  -.0003061   .0003244    -0.94   0.347    -.0009473    .0003352
        single |  -.0257255   .0135891    -1.89   0.060     -.052587    .0011359
         divor |  -.0023075    .012018    -0.19   0.848    -.0260633    .0214483
       norelig |  -.0171068   .0223175    -0.77   0.445    -.0612217    .0270081
       protest |  -.0027453   .0142278    -0.19   0.847    -.0308692    .0253786
           com |   -.022682   .0165302    -1.37   0.172    -.0553572    .0099932
          prof |   .1144831   .0936421     1.22   0.224    -.0706186    .2995848
       comform |   .0020944   .0276863     0.08   0.940    -.0526329    .0568217
      econfood |   .0035863   .0051898     0.69   0.491    -.0066723    .0138449
         house |  -.0274673   .0243291    -1.13   0.261    -.0755584    .0206239
          oven |   -.011539   .0165603    -0.70   0.487    -.0442736    .0211955
        lchang |   .0464442    .045555     1.02   0.310    -.0436039    .1364923
        llomue |   .0201847   .0375584     0.54   0.592    -.0540568    .0944261
       lchuabo |   .0368859   .0463689     0.80   0.428    -.0547711    .1285428
      lchitewe |  -.0121137   .0395025    -0.31   0.760    -.0901981    .0659707
        lronga |   -.025107   .0196391    -1.28   0.203    -.0639274    .0137134
       chitsua |  -.0028638   .0254561    -0.11   0.911    -.0531826    .0474551
        living |   .0002645   .0055494     0.05   0.962     -.010705    .0112339
 _Iinterview_2 |   .0048092   .0179293     0.27   0.789    -.0306314    .0402499
 _Iinterview_3 |  -.0028409   .0575293    -0.05   0.961    -.1165586    .1108767
 _Iinterview_4 |  -.0008241   .0544136    -0.02   0.988    -.1083829    .1067348
 _Iinterview_5 |          0  (omitted)
 _Iinterview_6 |  -.0223752   .0619587    -0.36   0.719    -.1448484    .1000981
 _Iinterview_7 |  -.0120027   .0567478    -0.21   0.833    -.1241755    .1001701
 _Iinterview_8 |  -.0021932    .056674    -0.04   0.969    -.1142203    .1098339
 _Iinterview_9 |    .015878   .0500652     0.32   0.752    -.0830855    .1148415
_Iinterview_10 |   .0237915   .0346433     0.69   0.493    -.0446877    .0922707
_Iinterview_11 |   .0101033   .0453229     0.22   0.824    -.0794861    .0996926
_Iinterview_12 |  -.0040996   .0344211    -0.12   0.905    -.0721395    .0639402
_Iinterview_13 |   .0232977    .039422     0.59   0.555    -.0546275    .1012229
_Iinterview_14 |   .0707649   .0640839     1.10   0.271    -.0559092    .1974391
_Iinterview_15 |  -.0812357    .067945    -1.20   0.234    -.2155421    .0530706
_Iinterview_16 |   -.030604   .0738727    -0.41   0.679    -.1766275    .1154195
_Iinterview_17 |  -.0712566   .0651898    -1.09   0.276    -.2001167    .0576035
_Iinterview_18 |  -.0668257   .0622956    -1.07   0.285    -.1899649    .0563135
_Iinterview_19 |  -.0258682   .0763315    -0.34   0.735    -.1767522    .1250157
_Iinterview_20 |          0  (omitted)
_Iinterview_21 |   .0154067   .0159728     0.96   0.336    -.0161667    .0469801
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |    .068118   .0625755     1.09   0.278    -.0555745    .1918105
_Iinterview_24 |   .0058071   .0145993     0.40   0.691    -.0230512    .0346654
_Iinterview_25 |   .0100159   .0269879     0.37   0.711    -.0433308    .0633626
_Iinterview_26 |   .0106382    .017962     0.59   0.555    -.0248672    .0461436
_Iinterview_27 |  -.0002539   .0166933    -0.02   0.988    -.0332513    .0327435
_Iinterview_28 |          0  (omitted)
         _cons |   .0162438   .0406569     0.40   0.690    -.0641223    .0966099
--------------------------------------------------------------------------------
i.interviewer     _Iinterview_1-28    (naturally coded; _Iinterview_1 omitted)
note: _Iinterview_5 omitted because of collinearity
note: _Iinterview_20 omitted because of collinearity
note: _Iinterview_22 omitted because of collinearity
note: _Iinterview_28 omitted because of collinearity

      Source |       SS       df       MS              Number of obs =     407
-------------+------------------------------           F( 54,   352) =    0.86
       Model |  .573855518    54  .010626954           Prob > F      =  0.7519
    Residual |  4.36471942   352  .012399771           R-squared     =  0.1162
-------------+------------------------------           Adj R-squared = -0.0194
       Total |  4.93857494   406  .012163978           Root MSE      =  .11135

--------------------------------------------------------------------------------
       renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
     civiceduc |      .0003   .0191326     0.02   0.987    -.0373286    .0379286
       hotline |  -.0006795   .0178165    -0.04   0.970    -.0357197    .0343607
       verdade |  -.0144449     .01971    -0.73   0.464    -.0532091    .0243193
           pr1 |   .0558411   .0452354     1.23   0.218    -.0331245    .1448066
           pr2 |  -.0263313    .164994    -0.16   0.873    -.3508294    .2981668
           pr3 |  -.0192045   .2046711    -0.09   0.925    -.4217365    .3833276
          post |   -.034257   .0209908    -1.63   0.104      -.07554    .0070261
     post_miss |  -.0134409   .0301574    -0.45   0.656    -.0727524    .0458705
        health |  -.0008101   .0153479    -0.05   0.958    -.0309952     .029375
   health_miss |  -.0140997   .0541333    -0.26   0.795     -.120565    .0923656
        police |   .0269369   .0177438     1.52   0.130    -.0079602     .061834
   police_miss |  -.0191058   .0806472    -0.24   0.813    -.1777167    .1395051
           sex |   .0179295   .0122249     1.47   0.143    -.0061136    .0419725
           age |  -.0003061   .0005095    -0.60   0.548    -.0013081     .000696
        single |  -.0257255   .0163008    -1.58   0.115    -.0577848    .0063337
         divor |  -.0023075    .068523    -0.03   0.973    -.1370735    .1324584
       norelig |  -.0171068   .0346663    -0.49   0.622     -.085286    .0510724
       protest |  -.0027453   .0157246    -0.17   0.862    -.0336712    .0281806
           com |   -.022682    .032388    -0.70   0.484    -.0863804    .0410164
          prof |   .1144831   .0433376     2.64   0.009       .02925    .1997162
       comform |   .0020944   .0486546     0.04   0.966    -.0935958    .0977846
      econfood |   .0035863   .0056696     0.63   0.527    -.0075643    .0147369
         house |  -.0274673    .019585    -1.40   0.162    -.0659855     .011051
          oven |   -.011539   .0249829    -0.46   0.644    -.0606735    .0375955
        lchang |   .0464442   .0304864     1.52   0.129    -.0135142    .1064026
        llomue |   .0201847   .0275157     0.73   0.464    -.0339311    .0743004
       lchuabo |   .0368859   .0256646     1.44   0.152    -.0135893     .087361
      lchitewe |  -.0121137   .0548198    -0.22   0.825    -.1199291    .0957017
        lronga |   -.025107   .0233713    -1.07   0.283     -.071072    .0208579
       chitsua |  -.0028638   .0607319    -0.05   0.962    -.1223069    .1165793
        living |   .0002645   .0057399     0.05   0.963    -.0110244    .0115533
 _Iinterview_2 |   .0048092   .0830418     0.06   0.954    -.1585113    .1681298
 _Iinterview_3 |  -.0028409   .2036213    -0.01   0.989    -.4033084    .3976265
 _Iinterview_4 |  -.0008241   .2029704    -0.00   0.997    -.4000112    .3983631
 _Iinterview_5 |          0  (omitted)
 _Iinterview_6 |  -.0223752   .2060433    -0.11   0.914    -.4276059    .3828556
 _Iinterview_7 |  -.0120027   .2006042    -0.06   0.952    -.4065363    .3825309
 _Iinterview_8 |  -.0021932    .203824    -0.01   0.991    -.4030592    .3986728
 _Iinterview_9 |    .015878   .1648713     0.10   0.923    -.3083786    .3401346
_Iinterview_10 |   .0237915    .115922     0.21   0.838    -.2041953    .2517783
_Iinterview_11 |   .0101033   .1660006     0.06   0.952    -.3163745     .336581
_Iinterview_12 |  -.0040996   .1655176    -0.02   0.980    -.3296275    .3214282
_Iinterview_13 |   .0232977   .1636747     0.14   0.887    -.2986056     .345201
_Iinterview_14 |   .0707649   .1646732     0.43   0.668    -.2531022    .3946321
_Iinterview_15 |  -.0812357   .0525336    -1.55   0.123     -.184555    .0220835
_Iinterview_16 |   -.030604   .0414761    -0.74   0.461    -.1121762    .0509682
_Iinterview_17 |  -.0712566   .0406197    -1.75   0.080    -.1511445    .0086313
_Iinterview_18 |  -.0668257   .0395952    -1.69   0.092    -.1446985    .0110472
_Iinterview_19 |  -.0258682   .0391797    -0.66   0.510     -.102924    .0511876
_Iinterview_20 |          0  (omitted)
_Iinterview_21 |   .0154067    .036682     0.42   0.675    -.0567366    .0875501
_Iinterview_22 |          0  (omitted)
_Iinterview_23 |    .068118   .0365081     1.87   0.063    -.0036835    .1399195
_Iinterview_24 |   .0058071   .0342395     0.17   0.865    -.0615326    .0731467
_Iinterview_25 |   .0100159   .0629332     0.16   0.874    -.1137565    .1337883
_Iinterview_26 |   .0106382   .0529524     0.20   0.841    -.0935047    .1147812
_Iinterview_27 |  -.0002539   .0454593    -0.01   0.996    -.0896598     .089152
_Iinterview_28 |          0  (omitted)
         _cons |   .0162438   .0380422     0.43   0.670    -.0585748    .0910624
--------------------------------------------------------------------------------

Simultaneous results for renamo2_4_2a, renamo2_4_3a

                                                  Number of obs   =       1031

                                         (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------------
                   |               Robust
                   |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
renamo2_4_2a_mean  |
         civiceduc |  -.0114102     .01093    -1.04   0.297    -.0328327    .0100122
           hotline |   .0072279   .0131866     0.55   0.584    -.0186175    .0330732
           verdade |  -.0002758   .0116367    -0.02   0.981    -.0230834    .0225318
               pr1 |  -.0217675   .0407713    -0.53   0.593    -.1016778    .0581428
               pr2 |  -.0240037   .0220413    -1.09   0.276    -.0672039    .0191965
               pr3 |  -.0303244   .0286269    -1.06   0.289     -.086432    .0257833
              post |  -.0185921   .0140184    -1.33   0.185    -.0460676    .0088833
         post_miss |  -.0080806   .0095188    -0.85   0.396    -.0267371    .0105759
            health |   .0053072   .0098131     0.54   0.589    -.0139262    .0245406
       health_miss |  -.0106899   .0131911    -0.81   0.418     -.036544    .0151643
            police |  -.0008489   .0112198    -0.08   0.940    -.0228393    .0211415
       police_miss |   .0558421   .0403807     1.38   0.167    -.0233026    .1349868
               sex |   .0123972   .0089073     1.39   0.164    -.0050608    .0298552
               age |  -.0002569   .0003378    -0.76   0.447     -.000919    .0004051
            single |  -.0036914   .0122157    -0.30   0.763    -.0276337    .0202509
             divor |   .1477205   .1204276     1.23   0.220    -.0883133    .3837543
           norelig |   .0239184   .0265485     0.90   0.368    -.0281156    .0759524
           protest |   .0187003   .0114416     1.63   0.102    -.0037249    .0411254
               com |  -.0022314   .0261792    -0.09   0.932    -.0535415    .0490788
              prof |  -.0151944   .0130938    -1.16   0.246    -.0408577    .0104689
           comform |  -.0206705   .0204248    -1.01   0.312    -.0607024    .0193614
          econfood |   .0025323   .0041528     0.61   0.542    -.0056071    .0106717
             house |   -.018052   .0174081    -1.04   0.300    -.0521713    .0160672
              oven |  -.0086926   .0078606    -1.11   0.269    -.0240991    .0067139
            lchang |   .0030502    .008348     0.37   0.715    -.0133115     .019412
            llomue |   .0101373   .0246384     0.41   0.681    -.0381531    .0584276
           lchuabo |   .0501336   .0301246     1.66   0.096    -.0089095    .1091767
          lchitewe |  -.0458305   .0213125    -2.15   0.032    -.0876023   -.0040588
            lronga |  -.0256778    .014016    -1.83   0.067    -.0531488    .0017931
           chitsua |  -.0215228   .0122327    -1.76   0.079    -.0454984    .0024528
            living |  -.0042157   .0045335    -0.93   0.352    -.0131013    .0046698
     _Iinterview_2 |    .006846   .0116256     0.59   0.556    -.0159397    .0296317
     _Iinterview_3 |   .0215832   .0275996     0.78   0.434    -.0325111    .0756775
     _Iinterview_4 |   .0191128   .0230657     0.83   0.407    -.0260951    .0643206
     _Iinterview_5 |   .0147193   .0274118     0.54   0.591    -.0390068    .0684454
     _Iinterview_6 |   .0128521   .0280164     0.46   0.646     -.042059    .0677632
     _Iinterview_7 |   .0406619   .0232721     1.75   0.081    -.0049505    .0862744
     _Iinterview_8 |   .0259414   .0269766     0.96   0.336    -.0269318    .0788145
     _Iinterview_9 |   .0234801   .0186899     1.26   0.209    -.0131514    .0601116
    _Iinterview_10 |   .0337115   .0259146     1.30   0.193    -.0170802    .0845032
    _Iinterview_11 |   .0474653   .0312165     1.52   0.128    -.0137179    .1086485
    _Iinterview_12 |   .0471697   .0373115     1.26   0.206    -.0259595    .1202989
    _Iinterview_13 |   .0158858   .0201968     0.79   0.432    -.0236991    .0554708
    _Iinterview_14 |   .0629366   .0383346     1.64   0.101    -.0121978    .1380711
    _Iinterview_15 |  -.0052138   .0328601    -0.16   0.874    -.0696185    .0591909
    _Iinterview_16 |   .0230213   .0386882     0.60   0.552    -.0528061    .0988487
    _Iinterview_17 |   .0529558   .0449368     1.18   0.239    -.0351186    .1410302
    _Iinterview_18 |  -.0145606   .0310916    -0.47   0.640    -.0754991    .0463778
    _Iinterview_19 |   .0499489   .0454179     1.10   0.271    -.0390686    .1389664
    _Iinterview_20 |   .0205438   .0300278     0.68   0.494    -.0383097    .0793972
    _Iinterview_21 |   .0001971   .0075495     0.03   0.979    -.0145995    .0149938
    _Iinterview_22 |          0  (omitted)
    _Iinterview_23 |    .022073     .02666     0.83   0.408    -.0301797    .0743256
    _Iinterview_24 |   -.002175   .0055927    -0.39   0.697    -.0131364    .0087864
    _Iinterview_25 |   .0063478   .0147675     0.43   0.667     -.022596    .0352916
    _Iinterview_26 |   .0592533   .0537341     1.10   0.270    -.0460636    .1645702
    _Iinterview_27 |  -.0026956   .0080529    -0.33   0.738    -.0184789    .0130878
    _Iinterview_28 |  -.0522012   .0410495    -1.27   0.203    -.1326568    .0282543
             _cons |   .0289512   .0274433     1.05   0.291    -.0248367    .0827392
-------------------+----------------------------------------------------------------
renamo2_4_2a_lnvar |
             _cons |   -4.15503   .2218588   -18.73   0.000    -4.589865   -3.720195
-------------------+----------------------------------------------------------------
renamo2_4_3a_mean  |
         civiceduc |      .0003   .0234322     0.01   0.990    -.0456263    .0462263
           hotline |  -.0006795   .0139319    -0.05   0.961    -.0279855    .0266266
           verdade |  -.0144449   .0126087    -1.15   0.252    -.0391574    .0102676
               pr1 |   .0558411   .0680782     0.82   0.412    -.0775898    .1892719
               pr2 |  -.0263313   .0717668    -0.37   0.714    -.1669917     .114329
               pr3 |  -.0192045   .0808453    -0.24   0.812    -.1776584    .1392495
              post |   -.034257    .025297    -1.35   0.176    -.0838381    .0153242
         post_miss |  -.0134409   .0113534    -1.18   0.236    -.0356932    .0088114
            health |  -.0008101   .0181048    -0.04   0.964    -.0362948    .0346746
       health_miss |  -.0140997   .0332826    -0.42   0.672    -.0793325    .0511331
            police |   .0269369   .0240428     1.12   0.263     -.020186    .0740599
       police_miss |  -.0191058   .0400497    -0.48   0.633    -.0976018    .0593902
               sex |   .0179295   .0123913     1.45   0.148     -.006357    .0422159
               age |  -.0003061    .000302    -1.01   0.311    -.0008979    .0002858
            single |  -.0257255   .0126485    -2.03   0.042    -.0505161    -.000935
             divor |  -.0023075   .0111861    -0.21   0.837    -.0242319    .0196168
           norelig |  -.0171068   .0207728    -0.82   0.410    -.0578206    .0236071
           protest |  -.0027453   .0132429    -0.21   0.836     -.028701    .0232104
               com |   -.022682    .015386    -1.47   0.140    -.0528381    .0074741
              prof |   .1144831   .0871604     1.31   0.189    -.0563482    .2853144
           comform |   .0020944   .0257699     0.08   0.935    -.0484137    .0526025
          econfood |   .0035863   .0048306     0.74   0.458    -.0058814     .013054
             house |  -.0274673   .0226451    -1.21   0.225    -.0718509    .0169163
              oven |   -.011539    .015414    -0.75   0.454    -.0417499    .0186719
            lchang |   .0464442   .0424017     1.10   0.273    -.0366616    .1295501
            llomue |   .0201847   .0349587     0.58   0.564    -.0483332    .0887025
           lchuabo |   .0368859   .0431593     0.85   0.393    -.0477048    .1214766
          lchitewe |  -.0121137   .0367683    -0.33   0.742    -.0841782    .0599508
            lronga |   -.025107   .0182797    -1.37   0.170    -.0609346    .0107205
           chitsua |  -.0028638   .0236941    -0.12   0.904    -.0493033    .0435758
            living |   .0002645   .0051653     0.05   0.959    -.0098593    .0103882
     _Iinterview_2 |   .0048092   .0166882     0.29   0.773    -.0278991    .0375176
     _Iinterview_3 |  -.0028409   .0535472    -0.05   0.958    -.1077915    .1021097
     _Iinterview_4 |  -.0008241   .0506472    -0.02   0.987    -.1000907    .0984426
     _Iinterview_5 |          0  (omitted)
     _Iinterview_6 |  -.0223752     .05767    -0.39   0.698    -.1354063     .090656
     _Iinterview_7 |  -.0120027   .0528198    -0.23   0.820    -.1155276    .0915222
     _Iinterview_8 |  -.0021932   .0527512    -0.04   0.967    -.1055835    .1011972
     _Iinterview_9 |    .015878   .0465998     0.34   0.733    -.0754559     .107212
    _Iinterview_10 |   .0237915   .0322454     0.74   0.461    -.0394083    .0869913
    _Iinterview_11 |   .0101033   .0421857     0.24   0.811    -.0725792    .0927857
    _Iinterview_12 |  -.0040996   .0320385    -0.13   0.898     -.066894    .0586947
    _Iinterview_13 |   .0232977   .0366933     0.63   0.525    -.0486199    .0952153
    _Iinterview_14 |   .0707649   .0596482     1.19   0.235    -.0461433    .1876732
    _Iinterview_15 |  -.0812357    .063242    -1.28   0.199    -.2051878    .0427163
    _Iinterview_16 |   -.030604   .0687593    -0.45   0.656    -.1653699    .1041618
    _Iinterview_17 |  -.0712566   .0606775    -1.17   0.240    -.1901823    .0476691
    _Iinterview_18 |  -.0668257   .0579836    -1.15   0.249    -.1804715    .0468201
    _Iinterview_19 |  -.0258682    .071048    -0.36   0.716    -.1651198    .1133834
    _Iinterview_20 |          0  (omitted)
    _Iinterview_21 |   .0154067   .0148672     1.04   0.300    -.0137325     .044546
    _Iinterview_22 |          0  (omitted)
    _Iinterview_23 |    .068118   .0582442     1.17   0.242    -.0460385    .1822745
    _Iinterview_24 |   .0058071   .0135887     0.43   0.669    -.0208264    .0324405
    _Iinterview_25 |   .0100159   .0251198     0.40   0.690    -.0392181    .0592498
    _Iinterview_26 |   .0106382   .0167187     0.64   0.525    -.0221299    .0434063
    _Iinterview_27 |  -.0002539   .0155378    -0.02   0.987    -.0307074    .0301996
    _Iinterview_28 |          0  (omitted)
             _cons |   .0162438   .0378427     0.43   0.668    -.0579265    .0904141
-------------------+----------------------------------------------------------------
renamo2_4_3a_lnvar |
             _cons |  -4.390077   .3420716   -12.83   0.000    -5.060525   -3.719629
------------------------------------------------------------------------------------

 ( 1)  [renamo2_4_2a_mean]civiceduc - [renamo2_4_3a_mean]civiceduc = 0

           chi2(  1) =    0.41
         Prob > chi2 =    0.5217
.52173965

 ( 1)  [renamo2_4_2a_mean]hotline - [renamo2_4_3a_mean]hotline = 0

           chi2(  1) =    0.23
         Prob > chi2 =    0.6294
.62940169

 ( 1)  [renamo2_4_2a_mean]verdade - [renamo2_4_3a_mean]verdade = 0

           chi2(  1) =    1.64
         Prob > chi2 =    0.2007
.2006756

. 
. matrix define means=(m_renamo2_2_1, m_renamo2_3_1, m_renamo2_4_1 \ t_renamo2_2_1_1, t_renamo2_
> 3_1_1, t_renamo2_4_1_1 \ t_renamo2_2_1_2, t_renamo2_3_1_2, t_renamo2_4_1_2 \ t_renamo2_2_1_3, 
> t_renamo2_3_1_3, t_renamo2_4_1_3 \ t_renamo2_2_1_4, t_renamo2_3_1_4, t_renamo2_4_1_4 \ t_renam
> o2_2_5, t_renamo2_3_5, t_renamo2_4_5 \ t_renamo2_2_6, t_renamo2_3_6, t_renamo2_4_6 \ t_renamo2
> _2_7, t_renamo2_3_7, t_renamo2_4_7)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_voting.xml") append sheet("voting
>  5") 


note: results saved to outputregs_voting.xml

. xml_tab $list2, save("outputregs_voting.xml") append sheet("voting 5 stats") 


note: results saved to outputregs_voting.xml

. estimates clear

. 
. *******************************************************************
. *****  TABLE 3: REGRESSIONS OF ELECTORAL PROBLEMS - EA LEVEL  *****
. *******************************************************************
. 
. global out1="serious_inc serious_int"

. global out2="eday_inc camp_inc viol_inc"

. 
. global ea="count2009 policesta policesta_miss sewer sewer_miss recreation recreation_miss road
>  road_miss"

. 
. global list1=""

. global list2=""

. 
. foreach i in $out1 {
  2. 
.         regress `i' $treat $prov if v==1 & time==1
  3.         estimates store `i'_2
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2=r(mean)
  6.         display m_`i'_2
  7.         test civiceduc = hotline
  8.         scalar define t1_`i'_2=r(p)
  9.         display t1_`i'_2
 10.         test civiceduc = verdade
 11.         scalar define t2_`i'_2=r(p)
 12.         display t2_`i'_2
 13.         test hotline = verdade
 14.         scalar define t3_`i'_2=r(p)
 15.         display t3_`i'_2
 16.         test civiceduc hotline verdade
 17.         scalar define t4_`i'_2=r(p)
 18.         display t4_`i'_2
 19.         
.         regress `i' $treat $prov $ea if v==1 & time==1
 20.         estimates store `i'_3
 21.         sum `i' if e(sample) & control == 1
 22.         scalar define m_`i'_3=r(mean)
 23.         display m_`i'_3
 24.         test civiceduc = hotline
 25.         scalar define t1_`i'_3=r(p)
 26.         display t1_`i'_3
 27.         test civiceduc = verdade
 28.         scalar define t2_`i'_3=r(p)
 29.         display t2_`i'_3
 30.         test hotline = verdade
 31.         scalar define t3_`i'_3=r(p)
 32.         display t3_`i'_3
 33.         test civiceduc hotline verdade
 34.         scalar define t4_`i'_3=r(p)
 35.         display t4_`i'_3
 36.         
.         global list1="$list1" + " `i'_2" + " `i'_3"
 37. 
. }

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  6,   154) =    5.81
       Model |  67.8531563     6  11.3088594           Prob > F      =  0.0000
    Residual |  299.836285   154  1.94698886           R-squared     =  0.1845
-------------+------------------------------           Adj R-squared =  0.1528
       Total |  367.689441   160  2.29805901           Root MSE      =  1.3953

------------------------------------------------------------------------------
 serious_inc |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.3262766   .3082731    -1.06   0.292    -.9352665    .2827133
     hotline |  -.1411286   .3101348    -0.46   0.650    -.7537961     .471539
     verdade |  -.5875114   .3121549    -1.88   0.062     -1.20417    .0291468
         pr1 |   1.633871   .3101348     5.27   0.000     1.021204    2.246539
         pr2 |   .4338714   .3101348     1.40   0.164    -.1787961    1.046539
         pr3 |   .3773406   .3102318     1.22   0.226    -.2355187    .9901999
       _cons |   .3548577   .2856813     1.24   0.216    -.2095023    .9192177
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 serious_inc |        41    .9512195    2.132501          0         10
.95121951

 ( 1)  civiceduc - hotline = 0

       F(  1,   154) =    0.36
            Prob > F =    0.5514
.55139004

 ( 1)  civiceduc - verdade = 0

       F(  1,   154) =    0.70
            Prob > F =    0.4041
.4041059

 ( 1)  hotline - verdade = 0

       F(  1,   154) =    2.02
            Prob > F =    0.1572
.15721727

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   154) =    1.32
            Prob > F =    0.2706
.27059554

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 15,   145) =    3.01
       Model |   87.415996    15  5.82773306           Prob > F      =  0.0003
    Residual |  280.273445   145  1.93292031           R-squared     =  0.2377
-------------+------------------------------           Adj R-squared =  0.1589
       Total |  367.689441   160  2.29805901           Root MSE      =  1.3903

---------------------------------------------------------------------------------
    serious_inc |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      civiceduc |  -.3457646   .3108871    -1.11   0.268    -.9602205    .2686912
        hotline |  -.0888257   .3119775    -0.28   0.776    -.7054366    .5277851
        verdade |  -.5775017   .3169697    -1.82   0.071    -1.203979     .048976
            pr1 |   1.816554    .340332     5.34   0.000     1.143901    2.489206
            pr2 |   .5005715   .3797227     1.32   0.189     -.249935    1.251078
            pr3 |   .3181328    .338502     0.94   0.349    -.3509027    .9871683
      count2009 |  -.0363278   .0480542    -0.76   0.451    -.1313049    .0586492
      policesta |  -.1720864   .2464493    -0.70   0.486    -.6591834    .3150106
 policesta_miss |   1.768014   1.470893     1.20   0.231    -1.139148    4.675175
          sewer |    .559255   .3789756     1.48   0.142     -.189775    1.308285
     sewer_miss |  -.3041762   2.267231    -0.13   0.893    -4.785266    4.176913
     recreation |   .4561921    .303682     1.50   0.135     -.144023    1.056407
recreation_miss |    -.17212   .6815266    -0.25   0.801     -1.51913     1.17489
           road |  -.4029441   .3002889    -1.34   0.182     -.996453    .1905648
      road_miss |  -.0090326   1.436126    -0.01   0.995    -2.847477    2.829412
          _cons |   .1796751    .418072     0.43   0.668    -.6466274    1.005978
---------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 serious_inc |        41    .9512195    2.132501          0         10
.95121951

 ( 1)  civiceduc - hotline = 0

       F(  1,   145) =    0.67
            Prob > F =    0.4142
.41415958

 ( 1)  civiceduc - verdade = 0

       F(  1,   145) =    0.52
            Prob > F =    0.4710
.47104272

 ( 1)  hotline - verdade = 0

       F(  1,   145) =    2.31
            Prob > F =    0.1310
.13096819

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   145) =    1.35
            Prob > F =    0.2611
.26105077

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  6,   154) =    7.74
       Model |  49.8235127     6  8.30391878           Prob > F      =  0.0000
    Residual |  165.176066   154  1.07257186           R-squared     =  0.2317
-------------+------------------------------           Adj R-squared =  0.2018
       Total |  214.999579   160  1.34374737           Root MSE      =  1.0357

------------------------------------------------------------------------------
 serious_int |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0556753   .2288059    -0.24   0.808    -.5076785     .396328
     hotline |   .1015092   .2301876     0.44   0.660    -.3532237    .5562421
     verdade |  -.3990575    .231687    -1.72   0.087    -.8567524    .0586373
         pr1 |   1.460319   .2301876     6.34   0.000     1.005586    1.915052
         pr2 |   .7475806   .2301876     3.25   0.001     .2928478    1.202314
         pr3 |   .5231627   .2302596     2.27   0.024     .0682876    .9780379
       _cons |   .1532253   .2120378     0.72   0.471    -.2656529    .5721034
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 serious_int |        41     .819338    1.262145          0          4
.81933798

 ( 1)  civiceduc - hotline = 0

       F(  1,   154) =    0.47
            Prob > F =    0.4957
.495726

 ( 1)  civiceduc - verdade = 0

       F(  1,   154) =    2.20
            Prob > F =    0.1405
.14048136

 ( 1)  hotline - verdade = 0

       F(  1,   154) =    4.61
            Prob > F =    0.0333
.03331347

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   154) =    1.73
            Prob > F =    0.1631
.16309722

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 15,   145) =    3.29
       Model |  54.6115549    15  3.64077033           Prob > F      =  0.0001
    Residual |  160.388024   145   1.1061243           R-squared     =  0.2540
-------------+------------------------------           Adj R-squared =  0.1768
       Total |  214.999579   160  1.34374737           Root MSE      =  1.0517

---------------------------------------------------------------------------------
    serious_int |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      civiceduc |  -.0612663   .2351786    -0.26   0.795    -.5260872    .4035546
        hotline |   .1094725   .2360034     0.46   0.643    -.3569786    .5759237
        verdade |  -.4659631   .2397799    -1.94   0.054    -.9398783    .0079521
            pr1 |    1.29891   .2574529     5.05   0.000     .7900651    1.807756
            pr2 |    .626904    .287251     2.18   0.031      .059164    1.194644
            pr3 |   .5402555   .2560685     2.11   0.037     .0341464    1.046365
      count2009 |    .042805   .0363518     1.18   0.241    -.0290429    .1146528
      policesta |   .0454993   .1864329     0.24   0.808    -.3229778    .4139763
 policesta_miss |   .9714204   1.112695     0.87   0.384    -1.227776    3.170617
          sewer |   -.085438   .2866859    -0.30   0.766     -.652061     .481185
     sewer_miss |  -.4695085   1.715105    -0.27   0.785    -3.859344    2.920327
     recreation |   -.268611    .229728    -1.17   0.244    -.7226592    .1854372
recreation_miss |  -.5707858   .5155583    -1.11   0.270    -1.589766    .4481944
           road |   -.037695   .2271613    -0.17   0.868    -.4866701      .41128
      road_miss |  -.0950418   1.086394    -0.09   0.930    -2.242256    2.052172
          _cons |    .251005   .3162613     0.79   0.429    -.3740728    .8760827
---------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 serious_int |        41     .819338    1.262145          0          4
.81933798

 ( 1)  civiceduc - hotline = 0

       F(  1,   145) =    0.52
            Prob > F =    0.4731
.4730587

 ( 1)  civiceduc - verdade = 0

       F(  1,   145) =    2.78
            Prob > F =    0.0974
.09740727

 ( 1)  hotline - verdade = 0

       F(  1,   145) =    5.59
            Prob > F =    0.0194
.01938848

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   145) =    2.11
            Prob > F =    0.1014
.10139546

. 
. matrix define means=(m_serious_inc_2, m_serious_inc_3, m_serious_int_2, m_serious_int_3 \ t1_s
> erious_inc_2, t1_serious_inc_3, t1_serious_int_2, t1_serious_int_3 \ t2_serious_inc_2, t2_seri
> ous_inc_3, t2_serious_int_2, t2_serious_int_3 \ t3_serious_inc_2, t3_serious_inc_3, t3_serious
> _int_2, t3_serious_int_3 \ t4_serious_inc_2, t4_serious_inc_3, t4_serious_int_2, t4_serious_in
> t_3)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_problems.xml") replace sheet("any
> prob") 


note: results saved to outputregs_problems.xml

. xml_tab $list2, save("outputregs_problems.xml") append sheet("anyprob stats") 


note: results saved to outputregs_problems.xml

. estimates clear

. 
. global list1=""

. global list2=""

. 
. foreach i in $out2 {
  2. 
.         regress `i' $treat $prov if v==1 & time==1
  3.         estimates store `i'_2
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2=r(mean)
  6.         display m_`i'_2
  7.         test civiceduc = hotline
  8.         scalar define t1_`i'_2=r(p)
  9.         display t1_`i'_2
 10.         test civiceduc = verdade
 11.         scalar define t2_`i'_2=r(p)
 12.         display t2_`i'_2
 13.         test hotline = verdade
 14.         scalar define t3_`i'_2=r(p)
 15.         display t3_`i'_2
 16.         test civiceduc hotline verdade
 17.         scalar define t4_`i'_2=r(p)
 18.         display t4_`i'_2
 19.         
.         regress `i' $treat $prov $ea if v==1 & time==1
 20.         estimates store `i'_3
 21.         sum `i' if e(sample) & control == 1
 22.         scalar define m_`i'_3=r(mean)
 23.         display m_`i'_3
 24.         test civiceduc = hotline
 25.         scalar define t1_`i'_3=r(p)
 26.         display t1_`i'_3
 27.         test civiceduc = verdade
 28.         scalar define t2_`i'_3=r(p)
 29.         display t2_`i'_3
 30.         test hotline = verdade
 31.         scalar define t3_`i'_3=r(p)
 32.         display t3_`i'_3
 33.         test civiceduc hotline verdade
 34.         scalar define t4_`i'_3=r(p)
 35.         display t4_`i'_3
 36.         
.         global list1="$list1" + " `i'_2" + " `i'_3"
 37. 
. }

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  6,   154) =   22.24
       Model |   47.789025     6  7.96483751           Prob > F      =  0.0000
    Residual |  55.1550743   154  .358149833           R-squared     =  0.4642
-------------+------------------------------           Adj R-squared =  0.4433
       Total |  102.944099   160  .643400621           Root MSE      =  .59846

------------------------------------------------------------------------------
    eday_inc |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0031221   .1322167    -0.02   0.981    -.2643146    .2580704
     hotline |   .0014879   .1330152     0.01   0.991    -.2612819    .2642578
     verdade |   .0576164   .1338816     0.43   0.668     -.206865    .3220978
         pr1 |  -.0485121   .1330152    -0.36   0.716    -.3112819    .2142578
         pr2 |   1.276488   .1330152     9.60   0.000     1.013718    1.539258
         pr3 |   .1280064   .1330568     0.96   0.338    -.1348457    .3908585
       _cons |   .0595165   .1225272     0.49   0.628    -.1825345    .3015675
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    eday_inc |        41    .3902439    .7706507          0          3
.3902439

 ( 1)  civiceduc - hotline = 0

       F(  1,   154) =    0.00
            Prob > F =    0.9724
.9723975

 ( 1)  civiceduc - verdade = 0

       F(  1,   154) =    0.21
            Prob > F =    0.6508
.65080443

 ( 1)  hotline - verdade = 0

       F(  1,   154) =    0.17
            Prob > F =    0.6775
.67746171

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   154) =    0.09
            Prob > F =    0.9636
.96355782

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 15,   145) =    9.55
       Model |  51.1502627    15  3.41001751           Prob > F      =  0.0000
    Residual |  51.7938367   145  .357198874           R-squared     =  0.4969
-------------+------------------------------           Adj R-squared =  0.4448
       Total |  102.944099   160  .643400621           Root MSE      =  .59766

---------------------------------------------------------------------------------
       eday_inc |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      civiceduc |  -.0102332   .1336444    -0.08   0.939     -.274376    .2539096
        hotline |   .0063713   .1341131     0.05   0.962    -.2586979    .2714405
        verdade |   .0108995   .1362592     0.08   0.936    -.2584112    .2802103
            pr1 |  -.1729381   .1463022    -1.18   0.239    -.4620985    .1162223
            pr2 |   1.105361   .1632355     6.77   0.000     .7827327    1.427989
            pr3 |   .1821332   .1455155     1.25   0.213    -.1054724    .4697387
      count2009 |   .0493826   .0206576     2.39   0.018     .0085537    .0902114
      policesta |  -.0948589   .1059438    -0.90   0.372    -.3042526    .1145347
 policesta_miss |   .5304462   .6323088     0.84   0.403    -.7192866    1.780179
          sewer |   .0866487   .1629144     0.53   0.596    -.2353449    .4086423
     sewer_miss |  -.6620931   .9746389    -0.68   0.498    -2.588428    1.264241
     recreation |  -.1353125   .1305471    -1.04   0.302    -.3933335    .1227085
recreation_miss |  -.1413565   .2929752    -0.48   0.630    -.7204101    .4376972
           road |  -.0143514   .1290885    -0.11   0.912    -.2694895    .2407867
      road_miss |   .0480944   .6173629     0.08   0.938    -1.172098    1.268287
          _cons |   .0441625   .1797211     0.25   0.806    -.3110491    .3993741
---------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    eday_inc |        41    .3902439    .7706507          0          3
.3902439

 ( 1)  civiceduc - hotline = 0

       F(  1,   145) =    0.02
            Prob > F =    0.9022
.90218798

 ( 1)  civiceduc - verdade = 0

       F(  1,   145) =    0.02
            Prob > F =    0.8784
.87837109

 ( 1)  hotline - verdade = 0

       F(  1,   145) =    0.00
            Prob > F =    0.9739
.97392662

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   145) =    0.01
            Prob > F =    0.9988
.99882445

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  6,   154) =    8.29
       Model |   70.855768     6  11.8092947           Prob > F      =  0.0000
    Residual |  219.318145   154   1.4241438           R-squared     =  0.2442
-------------+------------------------------           Adj R-squared =  0.2147
       Total |  290.173913   160  1.81358696           Root MSE      =  1.1934

------------------------------------------------------------------------------
    camp_inc |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.3500406   .2636519    -1.33   0.186    -.8708817    .1708005
     hotline |  -.2429317   .2652441    -0.92   0.361    -.7669182    .2810548
     verdade |  -.5662275   .2669718    -2.12   0.036    -1.093627    -.038828
         pr1 |   1.557068   .2652441     5.87   0.000     1.033082    2.081055
         pr2 |   .0320683   .2652441     0.12   0.904    -.4919182    .5560548
         pr3 |   .3516636   .2653271     1.33   0.187    -.1724868    .8758141
       _cons |   .2827317   .2443301     1.16   0.249    -.1999396    .7654029
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    camp_inc |        41    .7560976    2.070996          0         10
.75609756

 ( 1)  civiceduc - hotline = 0

       F(  1,   154) =    0.16
            Prob > F =    0.6869
.68691134

 ( 1)  civiceduc - verdade = 0

       F(  1,   154) =    0.66
            Prob > F =    0.4195
.41946427

 ( 1)  hotline - verdade = 0

       F(  1,   154) =    1.45
            Prob > F =    0.2306
.23055164

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   154) =    1.56
            Prob > F =    0.2019
.20186509

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 15,   145) =    4.41
       Model |  90.9382077    15  6.06254718           Prob > F      =  0.0000
    Residual |  199.235705   145  1.37403935           R-squared     =  0.3134
-------------+------------------------------           Adj R-squared =  0.2424
       Total |  290.173913   160  1.81358696           Root MSE      =  1.1722

---------------------------------------------------------------------------------
       camp_inc |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      civiceduc |  -.3664046   .2621171    -1.40   0.164    -.8844684    .1516593
        hotline |  -.2150873   .2630364    -0.82   0.415    -.7349681    .3047935
        verdade |  -.5079423   .2672454    -1.90   0.059    -1.036142    .0202575
            pr1 |   1.861263   .2869429     6.49   0.000     1.294132    2.428394
            pr2 |   .3061928   .3201541     0.96   0.340    -.3265789    .9389645
            pr3 |   .2441049   .2853999     0.86   0.394    -.3199765    .8081864
      count2009 |  -.0803184   .0405157    -1.98   0.049    -.1603961   -.0002407
      policesta |  -.0044956   .2077878    -0.02   0.983    -.4151799    .4061887
 policesta_miss |   1.265393   1.240149     1.02   0.309    -1.185711    3.716496
          sewer |   .3278638   .3195243     1.03   0.307     -.303663    .9593907
     sewer_miss |   .1960077   1.911562     0.10   0.918    -3.582117    3.974132
     recreation |   .5791155   .2560423     2.26   0.025     .0730584    1.085173
recreation_miss |   .1743355    .574613     0.30   0.762    -.9613638    1.310035
           road |  -.3650488   .2531815    -1.44   0.152    -.8654517    .1353542
      road_miss |  -.0473672   1.210835    -0.04   0.969    -2.440534      2.3458
          _cons |    .080107   .3524875     0.23   0.821    -.6165703    .7767844
---------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    camp_inc |        41    .7560976    2.070996          0         10
.75609756

 ( 1)  civiceduc - hotline = 0

       F(  1,   145) =    0.33
            Prob > F =    0.5682
.56818392

 ( 1)  civiceduc - verdade = 0

       F(  1,   145) =    0.27
            Prob > F =    0.6014
.60141607

 ( 1)  hotline - verdade = 0

       F(  1,   145) =    1.17
            Prob > F =    0.2821
.28211086

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   145) =    1.34
            Prob > F =    0.2640
.26396203

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F(  6,   154) =    4.22
       Model |  9.37338187     6  1.56223031           Prob > F      =  0.0006
    Residual |  57.0489784   154  .370447912           R-squared     =  0.1411
-------------+------------------------------           Adj R-squared =  0.1077
       Total |  66.4223602   160  .415139752           Root MSE      =  .60864

------------------------------------------------------------------------------
    viol_inc |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0756367   .1344676    -0.56   0.575    -.3412757    .1900024
     hotline |  -.0215428   .1352796    -0.16   0.874     -.288786    .2457005
     verdade |  -.1698031   .1361608    -1.25   0.214     -.438787    .0991809
         pr1 |   .6034572   .1352796     4.46   0.000      .336214    .8707005
         pr2 |   .1284572   .1352796     0.95   0.344     -.138786    .3957005
         pr3 |   .1011031   .1353219     0.75   0.456    -.1662238    .3684299
       _cons |   .1382884   .1246131     1.11   0.269    -.1078832    .3844601
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    viol_inc |        41    .3414634    .6931723          0          3
.34146341

 ( 1)  civiceduc - hotline = 0

       F(  1,   154) =    0.16
            Prob > F =    0.6898
.68980899

 ( 1)  civiceduc - verdade = 0

       F(  1,   154) =    0.48
            Prob > F =    0.4904
.49037616

 ( 1)  hotline - verdade = 0

       F(  1,   154) =    1.17
            Prob > F =    0.2808
.28080046

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   154) =    0.61
            Prob > F =    0.6074
.60741218

      Source |       SS       df       MS              Number of obs =     161
-------------+------------------------------           F( 15,   145) =    2.30
       Model |   12.768135    15  .851208997           Prob > F      =  0.0059
    Residual |  53.6542253   145   .37002914           R-squared     =  0.1922
-------------+------------------------------           Adj R-squared =  0.1087
       Total |  66.4223602   160  .415139752           Root MSE      =   .6083

---------------------------------------------------------------------------------
       viol_inc |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
      civiceduc |  -.0693619   .1360234    -0.51   0.611    -.3382067    .1994829
        hotline |   .0149047   .1365005     0.11   0.913     -.254883    .2846924
        verdade |  -.1634357   .1386847    -1.18   0.241    -.4375405    .1106691
            pr1 |   .5774786   .1489065     3.88   0.000     .2831708    .8717863
            pr2 |  -.0441309   .1661413    -0.27   0.791    -.3725024    .2842406
            pr3 |    .069943   .1481059     0.47   0.637    -.2227823    .3626682
      count2009 |  -.0006327   .0210253    -0.03   0.976    -.0421883    .0409229
      policesta |  -.1087443   .1078297    -1.01   0.315    -.3218654    .1043768
 policesta_miss |   .0031656   .6435646     0.00   0.996    -1.268814    1.275145
          sewer |   .4129509   .1658144     2.49   0.014     .0852254    .7406763
     sewer_miss |  -.0080431   .9919886    -0.01   0.994    -1.968668    1.952582
     recreation |  -.0061046   .1328709    -0.05   0.963    -.2687187    .2565094
recreation_miss |  -.2682805   .2981905    -0.90   0.370     -.857642    .3210809
           road |  -.1329193   .1313864    -1.01   0.313    -.3925991    .1267606
      road_miss |  -.0300911   .6283526    -0.05   0.962    -1.272005    1.211822
          _cons |      .2162   .1829204     1.18   0.239    -.1453348    .5777347
---------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    viol_inc |        41    .3414634    .6931723          0          3
.34146341

 ( 1)  civiceduc - hotline = 0

       F(  1,   145) =    0.38
            Prob > F =    0.5403
.54026801

 ( 1)  civiceduc - verdade = 0

       F(  1,   145) =    0.45
            Prob > F =    0.5036
.50359835

 ( 1)  hotline - verdade = 0

       F(  1,   145) =    1.61
            Prob > F =    0.2072
.20722107

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   145) =    0.67
            Prob > F =    0.5704
.57036313

. 
. matrix define means=(m_eday_inc_2, m_eday_inc_3, m_camp_inc_2, m_camp_inc_3, m_viol_inc_2, m_v
> iol_inc_3 \ t1_eday_inc_2, t1_eday_inc_3, t1_camp_inc_2, t1_camp_inc_3, t1_viol_inc_2, t1_viol
> _inc_3 \ t2_eday_inc_2, t2_eday_inc_3, t2_camp_inc_2, t2_camp_inc_3, t2_viol_inc_2, t2_viol_in
> c_3 \ t3_eday_inc_2, t3_eday_inc_3, t3_camp_inc_2, t3_camp_inc_3, t3_viol_inc_2, t3_viol_inc_3
>  \ t4_eday_inc_2, t4_eday_inc_3, t4_camp_inc_2, t4_camp_inc_3, t4_viol_inc_2, t4_viol_inc_3)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_problems.xml") append sheet("spec
> prob") 


note: results saved to outputregs_problems.xml

. xml_tab $list2, save("outputregs_problems.xml") append sheet("specprob stats") 


note: results saved to outputregs_problems.xml

. estimates clear

. 
. ***************************************************************************************
. *****  TABLES 4 AND OA TABLE 10 (PART): REGRESSIONS OF MEDIATOR SURVEY OUTCOMES  *****
. ***************************************************************************************
. 
. global ea="post post_miss health health_miss"

. global controls="sex age single divor protest com prof tea comform dom econfood house llomue c
> hitsua living"

. 
. *info
. 
. global final="zzscinfo"

. 
. global list1=""

. global list2=""

. 
. foreach i in $final {
  2. 
.         regress `i' $treat $prov if time==1, cluster(ea)
  3.         estimates store `i'_2_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2_1=r(mean)
  6.         display m_`i'_2_1
  7.         test civiceduc = hotline
  8.         scalar define t_`i'_2_1_1=r(p)
  9.         display t_`i'_2_1_1
 10.         test civiceduc = verdade
 11.         scalar define t_`i'_2_1_2=r(p)
 12.         display t_`i'_2_1_2
 13.         test hotline = verdade
 14.         scalar define t_`i'_2_1_3=r(p)
 15.         display t_`i'_2_1_3
 16.         test civiceduc hotline verdade
 17.         scalar define t_`i'_2_1_4=r(p)
 18.         display t_`i'_2_1_4
 19. 
.         regress `i' $treat $prov if time==1 & lazy==0, cluster(ea)
 20.         estimates store `i'_2_2
 21.         regress `i' $treat $prov if time==1 & lazy==0
 22.         estimates store `i'_2_2a
 23.         
.         regress `i' $treat $prov if time==1 & (lazy==1|control==1), cluster(ea)
 24.         estimates store `i'_2_3
 25.         regress `i' $treat $prov if time==1 & (lazy==1|control==1)
 26.         estimates store `i'_2_3a
 27. 
.         suest `i'_2_2a `i'_2_3a, cluster(ea)
 28.         test [`i'_2_2a_mean]civiceduc=[`i'_2_3a_mean]civiceduc  
 29.         scalar define t_`i'_2_5=r(p)
 30.         display t_`i'_2_5
 31.         test [`i'_2_2a_mean]hotline=[`i'_2_3a_mean]hotline      
 32.         scalar define t_`i'_2_6=r(p)
 33.         display t_`i'_2_6
 34.         test [`i'_2_2a_mean]verdade=[`i'_2_3a_mean]verdade
 35.         scalar define t_`i'_2_7=r(p)
 36.         display t_`i'_2_7
 37. 
.         regress `i' $treat $prov $ea $controls if time==1, cluster(ea)
 38.         estimates store `i'_3_1
 39.         sum `i' if e(sample) & control == 1
 40.         scalar define m_`i'_3_1=r(mean)
 41.         display m_`i'_3_1
 42.         test civiceduc = hotline
 43.         scalar define t_`i'_3_1_1=r(p)
 44.         display t_`i'_3_1_1
 45.         test civiceduc = verdade
 46.         scalar define t_`i'_3_1_2=r(p)
 47.         display t_`i'_3_1_2
 48.         test hotline = verdade
 49.         scalar define t_`i'_3_1_3=r(p)
 50.         display t_`i'_3_1_3
 51.         test civiceduc hotline verdade
 52.         scalar define t_`i'_3_1_4=r(p)
 53.         display t_`i'_3_1_4
 54. 
.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0, cluster(ea)
 55.         estimates store `i'_3_2
 56.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0
 57.         estimates store `i'_3_2a
 58.         
.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1), cluster(ea)
 59.         estimates store `i'_3_3
 60.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1)
 61.         estimates store `i'_3_3a
 62. 
.         suest `i'_3_2a `i'_3_3a, cluster(ea)
 63.         test [`i'_3_2a_mean]civiceduc=[`i'_3_3a_mean]civiceduc  
 64.         scalar define t_`i'_3_5=r(p)
 65.         display t_`i'_3_5
 66.         test [`i'_3_2a_mean]hotline=[`i'_3_3a_mean]hotline      
 67.         scalar define t_`i'_3_6=r(p)
 68.         display t_`i'_3_6
 69.         test [`i'_3_2a_mean]verdade=[`i'_3_3a_mean]verdade
 70.         scalar define t_`i'_3_7=r(p)
 71.         display t_`i'_3_7
 72.         
.         global list1="$list1" + " `i'_2_1" + " `i'_3_1" + " `i'_2_2" + " `i'_3_2"  + " `i'_2_3
> " + " `i'_3_3"
 73.         
.         }

Linear regression                                      Number of obs =    1151
                                                       F(  6,   160) =   19.31
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0833
                                                       Root MSE      =  .59621

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zzscinfo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0739349   .0638206     1.16   0.248    -.0521045    .1999742
     hotline |   .1583414   .0630194     2.51   0.013     .0338844    .2827984
     verdade |   .1227515   .0677771     1.81   0.072    -.0111016    .2566047
         pr1 |  -.3776521   .0558082    -6.77   0.000    -.4878677   -.2674365
         pr2 |  -.3122656   .0638291    -4.89   0.000    -.4383218   -.1862094
         pr3 |  -.4314136   .0460893    -9.36   0.000    -.5224354   -.3403918
       _cons |   .2817277   .0493456     5.71   0.000     .1842749    .3791804
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    zzscinfo |       278    3.37e-08     .661451  -1.588012   .9682169
3.374e-08

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    2.15
            Prob > F =    0.1447
.14472744

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.60
            Prob > F =    0.4398
.43982919

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.32
            Prob > F =    0.5696
.56958034

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    2.31
            Prob > F =    0.0779
.07787975

Linear regression                                      Number of obs =     976
                                                       F(  6,   160) =   17.23
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0859
                                                       Root MSE      =  .59983

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zzscinfo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0657488   .0661998     0.99   0.322    -.0649894    .1964869
     hotline |   .1507434   .0624224     2.41   0.017     .0274653    .2740214
     verdade |   .1220476   .0674641     1.81   0.072    -.0111874    .2552825
         pr1 |  -.3986314   .0571773    -6.97   0.000     -.511551   -.2857119
         pr2 |  -.2905315   .0635338    -4.57   0.000    -.4160044   -.1650586
         pr3 |  -.4446214   .0512471    -8.68   0.000    -.5458294   -.3434135
       _cons |   .2847656   .0483316     5.89   0.000     .1893154    .3802158
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     976
-------------+------------------------------           F(  6,   969) =   15.17
       Model |  32.7581089     6  5.45968481           Prob > F      =  0.0000
    Residual |  348.642783   969  .359796473           R-squared     =  0.0859
-------------+------------------------------           Adj R-squared =  0.0802
       Total |  381.400892   975  .391180402           Root MSE      =  .59983

------------------------------------------------------------------------------
    zzscinfo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0657488   .0526247     1.25   0.212    -.0375228    .1690204
     hotline |   .1507434   .0532981     2.83   0.005     .0461503    .2553364
     verdade |   .1220476   .0540862     2.26   0.024      .015908    .2281872
         pr1 |  -.3986314   .0540728    -7.37   0.000    -.5047447   -.2925182
         pr2 |  -.2905315   .0541436    -5.37   0.000    -.3967837   -.1842793
         pr3 |  -.4446214   .0547796    -8.12   0.000    -.5521218    -.337121
       _cons |   .2847656    .049157     5.79   0.000     .1882991    .3812321
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     453
                                                       F(  6,   152) =    8.10
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0946
                                                       Root MSE      =  .61309

                                   (Std. Err. adjusted for 153 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zzscinfo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1159142   .1016492     1.14   0.256    -.0849135     .316742
     hotline |   .1854509   .0847595     2.19   0.030     .0179921    .3529096
     verdade |   .1252678   .0989683     1.27   0.208    -.0702634     .320799
         pr1 |  -.3686236   .0801022    -4.60   0.000     -.526881   -.2103663
         pr2 |  -.3099068   .0897492    -3.45   0.001    -.4872237   -.1325898
         pr3 |  -.4838986   .0901463    -5.37   0.000    -.6620001    -.305797
       _cons |   .2916575   .0491375     5.94   0.000     .1945768    .3887381
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     453
-------------+------------------------------           F(  6,   446) =    7.77
       Model |  17.5219884     6   2.9203314           Prob > F      =  0.0000
    Residual |  167.643694   446  .375882722           R-squared     =  0.0946
-------------+------------------------------           Adj R-squared =  0.0824
       Total |  185.165682   452  .409658589           Root MSE      =  .61309

------------------------------------------------------------------------------
    zzscinfo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1159142   .0912819     1.27   0.205    -.0634818    .2953102
     hotline |   .1854509   .0851823     2.18   0.030     .0180424    .3528594
     verdade |   .1252678   .0892712     1.40   0.161    -.0501767    .3007123
         pr1 |  -.3686236   .0811209    -4.54   0.000    -.5280504   -.2091969
         pr2 |  -.3099068   .0815471    -3.80   0.000    -.4701711   -.1496424
         pr3 |  -.4838986   .0808082    -5.99   0.000    -.6427108   -.3250864
       _cons |   .2916575   .0618826     4.71   0.000     .1700397    .4132752
------------------------------------------------------------------------------

Simultaneous results for zzscinfo_2_2a, zzscinfo_2_3a

                                                  Number of obs   =       1151

                                          (Std. Err. adjusted for 161 clusters in ea)
-------------------------------------------------------------------------------------
                    |               Robust
                    |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
zzscinfo_2_2a_mean  |
          civiceduc |   .0657488   .0659958     1.00   0.319    -.0636006    .1950982
            hotline |   .1507434     .06223     2.42   0.015     .0287748     .272712
            verdade |   .1220476   .0672562     1.81   0.070    -.0097722    .2538673
                pr1 |  -.3986314   .0570011    -6.99   0.000    -.5103516   -.2869113
                pr2 |  -.2905315    .063338    -4.59   0.000    -.4146716   -.1663913
                pr3 |  -.4446214   .0510892    -8.70   0.000    -.5447543   -.3444885
              _cons |   .2847656   .0481827     5.91   0.000     .1903293    .3792019
--------------------+----------------------------------------------------------------
zzscinfo_2_2a_lnvar |
              _cons |  -1.022217   .0512781   -19.93   0.000     -1.12272   -.9217135
--------------------+----------------------------------------------------------------
zzscinfo_2_3a_mean  |
          civiceduc |   .1159142   .1009558     1.15   0.251    -.0819555     .313784
            hotline |   .1854509   .0841813     2.20   0.028     .0204586    .3504431
            verdade |   .1252678   .0982932     1.27   0.203    -.0673834     .317919
                pr1 |  -.3686236   .0795557    -4.63   0.000      -.52455   -.2126972
                pr2 |  -.3099068    .089137    -3.48   0.001     -.484612   -.1352015
                pr3 |  -.4838986   .0895314    -5.40   0.000    -.6593769   -.3084203
              _cons |   .2916575   .0488023     5.98   0.000     .1960067    .3873082
--------------------+----------------------------------------------------------------
zzscinfo_2_3a_lnvar |
              _cons |  -.9784781   .0749378   -13.06   0.000    -1.125353   -.8316027
-------------------------------------------------------------------------------------

 ( 1)  [zzscinfo_2_2a_mean]civiceduc - [zzscinfo_2_3a_mean]civiceduc = 0

           chi2(  1) =    0.26
         Prob > chi2 =    0.6094
.60938586

 ( 1)  [zzscinfo_2_2a_mean]hotline - [zzscinfo_2_3a_mean]hotline = 0

           chi2(  1) =    0.30
         Prob > chi2 =    0.5830
.58299802

 ( 1)  [zzscinfo_2_2a_mean]verdade - [zzscinfo_2_3a_mean]verdade = 0

           chi2(  1) =    0.00
         Prob > chi2 =    0.9676
.96759486

Linear regression                                      Number of obs =    1135
                                                       F( 25,   160) =   17.44
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.2717
                                                       Root MSE      =  .53565

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zzscinfo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1550086   .0584239     2.65   0.009     .0396271    .2703901
     hotline |     .17652   .0576629     3.06   0.003     .0626414    .2903986
     verdade |   .1563039   .0632612     2.47   0.015     .0313693    .2812386
         pr1 |  -.3638127   .0587965    -6.19   0.000      -.47993   -.2476955
         pr2 |  -.2800178   .0644206    -4.35   0.000    -.4072421   -.1527934
         pr3 |  -.3494958   .0542425    -6.44   0.000    -.4566195   -.2423721
        post |    .041387   .0805376     0.51   0.608    -.1176669    .2004409
   post_miss |  -.0732318   .1224497    -0.60   0.551     -.315058    .1685944
      health |   .0886812   .0417456     2.12   0.035     .0062378    .1711246
 health_miss |   .1594904    .115418     1.38   0.169    -.0684488    .3874296
         sex |   .3288584   .0328623    10.01   0.000     .2639586    .3937583
         age |  -.0030999   .0015864    -1.95   0.052    -.0062329     .000033
      single |  -.0145149   .0411993    -0.35   0.725    -.0958794    .0668496
       divor |   .1050525   .1376729     0.76   0.447     -.166838    .3769429
     protest |  -.0353203   .0427577    -0.83   0.410    -.1197626     .049122
         com |  -.0321494   .0879477    -0.37   0.715    -.2058374    .1415386
        prof |   .4766915   .1320909     3.61   0.000     .2158249     .737558
         tea |   .6076757   .0532823    11.40   0.000     .5024484     .712903
     comform |   .0617746   .1063841     0.58   0.562    -.1483234    .2718727
         dom |   .0082769   .0547714     0.15   0.880    -.0998911     .116445
    econfood |  -.0924668   .0139681    -6.62   0.000    -.1200525   -.0648811
       house |   .0835864   .0440667     1.90   0.060     -.003441    .1706137
      llomue |  -.0180281    .074665    -0.24   0.810    -.1654842    .1294279
     chitsua |   .2509501   .1367011     1.84   0.068    -.0190211    .5209213
      living |   .0033419   .0174116     0.19   0.848    -.0310443    .0377281
       _cons |   .1093924   .0985233     1.11   0.269    -.0851813    .3039662
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    zzscinfo |       275    .0003757    .6594198  -1.588012   .9682169
.00037575

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.17
            Prob > F =    0.6776
.67760449

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.00
            Prob > F =    0.9808
.98084802

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.14
            Prob > F =    0.7136
.71359226

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    3.53
            Prob > F =    0.0162
.01619756

Linear regression                                      Number of obs =     965
                                                       F( 25,   160) =   16.83
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.2630
                                                       Root MSE      =  .54402

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zzscinfo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1587572   .0631009     2.52   0.013     .0341391    .2833754
     hotline |   .1670904   .0563569     2.96   0.003     .0557911    .2783897
     verdade |   .1590748   .0644704     2.47   0.015     .0317522    .2863975
         pr1 |    -.37691   .0613225    -6.15   0.000    -.4980159   -.2558042
         pr2 |  -.2587417   .0658822    -3.93   0.000    -.3888526   -.1286307
         pr3 |  -.3558362   .0568473    -6.26   0.000    -.4681041   -.2435683
        post |   .0355766   .0808496     0.44   0.661    -.1240934    .1952465
   post_miss |  -.0684071   .1265216    -0.54   0.589    -.3182749    .1814607
      health |   .0969048   .0437954     2.21   0.028     .0104132    .1833964
 health_miss |   .1443601   .1315937     1.10   0.274    -.1155245    .4042448
         sex |   .3227034   .0361304     8.93   0.000     .2513493    .3940574
         age |   -.003377   .0015463    -2.18   0.030    -.0064307   -.0003232
      single |   -.035347    .045017    -0.79   0.434    -.1242511    .0535571
       divor |   .0730867   .1785115     0.41   0.683     -.279456    .4256293
     protest |  -.0538541   .0421733    -1.28   0.203    -.1371423    .0294341
         com |  -.0579435   .0955149    -0.61   0.545     -.246576    .1306891
        prof |   .5533591   .1143662     4.84   0.000     .3274971    .7792212
         tea |   .5732219   .0591824     9.69   0.000     .4563425    .6901013
     comform |   .1569296   .1076635     1.46   0.147    -.0556952    .3695544
         dom |   .0236669   .0600339     0.39   0.694    -.0948941    .1422279
    econfood |  -.0878316   .0160739    -5.46   0.000     -.119576   -.0560873
       house |   .0896023   .0484895     1.85   0.066    -.0061596    .1853642
      llomue |   .0022977    .085772     0.03   0.979    -.1670936    .1716889
     chitsua |   .2490166   .1540029     1.62   0.108    -.0551239    .5531572
      living |   .0029251   .0183171     0.16   0.873    -.0332494    .0390996
       _cons |    .114374   .1041002     1.10   0.274    -.0912137    .3199617
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     965
-------------+------------------------------           F( 25,   939) =   13.41
       Model |  99.1843061    25  3.96737225           Prob > F      =  0.0000
    Residual |  277.900452   939  .295953623           R-squared     =  0.2630
-------------+------------------------------           Adj R-squared =  0.2434
       Total |  377.084758   964  .391166761           Root MSE      =  .54402

------------------------------------------------------------------------------
    zzscinfo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1587572   .0504144     3.15   0.002     .0598193    .2576952
     hotline |   .1670904   .0500819     3.34   0.001     .0688051    .2653757
     verdade |   .1590748     .05114     3.11   0.002      .058713    .2594367
         pr1 |    -.37691   .0604217    -6.24   0.000    -.4954872   -.2583328
         pr2 |  -.2587417    .058904    -4.39   0.000    -.3743404   -.1431429
         pr3 |  -.3558362   .0573485    -6.20   0.000    -.4683824   -.2432901
        post |   .0355766   .0607384     0.59   0.558    -.0836222    .1547753
   post_miss |  -.0684071   .1012883    -0.68   0.500    -.2671847    .1303704
      health |   .0969048    .042026     2.31   0.021     .0144291    .1793805
 health_miss |   .1443601   .1126486     1.28   0.200    -.0767119    .3654322
         sex |   .3227034   .0378371     8.53   0.000     .2484483    .3969585
         age |   -.003377   .0014609    -2.31   0.021     -.006244   -.0005099
      single |   -.035347   .0489185    -0.72   0.470    -.1313493    .0606553
       divor |   .0730867   .2097889     0.35   0.728    -.3386227    .4847961
     protest |  -.0538541   .0430437    -1.25   0.211    -.1383271    .0306188
         com |  -.0579435     .08378    -0.69   0.489    -.2223612    .1064743
        prof |   .5533591   .1437497     3.85   0.000     .2712512    .8354671
         tea |   .5732219    .087452     6.55   0.000     .4015979    .7448459
     comform |   .1569296   .1679515     0.93   0.350    -.1726741    .4865333
         dom |   .0236669   .0543776     0.44   0.663    -.0830488    .1303825
    econfood |  -.0878316   .0156367    -5.62   0.000    -.1185185   -.0571447
       house |   .0896023   .0514795     1.74   0.082    -.0114258    .1906305
      llomue |   .0022977   .0711541     0.03   0.974    -.1373417     .141937
     chitsua |   .2490166   .1621171     1.54   0.125    -.0691371    .5671704
      living |   .0029251   .0177287     0.16   0.869    -.0318673    .0377175
       _cons |    .114374   .0971024     1.18   0.239    -.0761888    .3049369
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     445
                                                       F( 25,   151) =   14.27
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.3386
                                                       Root MSE      =  .53416

                                   (Std. Err. adjusted for 152 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    zzscinfo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .156073   .0814819     1.92   0.057    -.0049188    .3170649
     hotline |   .2536549   .0839527     3.02   0.003     .0877811    .4195286
     verdade |   .1738021   .0849398     2.05   0.042     .0059781     .341626
         pr1 |  -.3608621   .0891876    -4.05   0.000    -.5370788   -.1846454
         pr2 |  -.2363035   .0955321    -2.47   0.014    -.4250558   -.0475512
         pr3 |  -.3335426   .1012894    -3.29   0.001      -.53367   -.1334152
        post |   .1374264   .0963152     1.43   0.156     -.052873    .3277258
   post_miss |  -.1243318    .185708    -0.67   0.504    -.4912535      .24259
      health |   .1545399   .0643795     2.40   0.018     .0273388    .2817409
 health_miss |    .368892   .1590942     2.32   0.022     .0545538    .6832301
         sex |   .3793593   .0451257     8.41   0.000        .2902    .4685185
         age |  -.0039619    .002482    -1.60   0.113    -.0088658    .0009421
      single |   .0317926   .0604426     0.53   0.600    -.0876299    .1512151
       divor |   .4316679   .1542952     2.80   0.006     .1268117    .7365241
     protest |  -.0569192   .0648444    -0.88   0.381    -.1850386    .0712002
         com |  -.0789702   .1366501    -0.58   0.564    -.3489633     .191023
        prof |   .3686356    .249974     1.47   0.142    -.1252627    .8625339
         tea |   .6863623    .064184    10.69   0.000     .5595477    .8131769
     comform |   .0003816   .1531483     0.00   0.998    -.3022087    .3029719
         dom |   .0403159   .0882334     0.46   0.648    -.1340155    .2146473
    econfood |  -.1015958   .0237174    -4.28   0.000    -.1484566   -.0547351
       house |   .1299587   .0817194     1.59   0.114    -.0315023    .2914198
      llomue |   .0664269   .1338094     0.50   0.620    -.1979535    .3308073
     chitsua |   .1797454   .1484132     1.21   0.228    -.1134892    .4729801
      living |   .0029908   .0252261     0.12   0.906    -.0468509    .0528324
       _cons |  -.0189457   .1311295    -0.14   0.885    -.2780312    .2401399
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     445
-------------+------------------------------           F( 25,   419) =    8.58
       Model |  61.2106703    25  2.44842681           Prob > F      =  0.0000
    Residual |  119.552168   419   .28532737           R-squared     =  0.3386
-------------+------------------------------           Adj R-squared =  0.2992
       Total |  180.762838   444   .40712351           Root MSE      =  .53416

------------------------------------------------------------------------------
    zzscinfo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .156073   .0840072     1.86   0.064     -.009055    .3212011
     hotline |   .2536549   .0783358     3.24   0.001     .0996747     .407635
     verdade |   .1738021    .082641     2.10   0.036     .0113594    .3362448
         pr1 |  -.3608621   .0904325    -3.99   0.000      -.53862   -.1831042
         pr2 |  -.2363035   .0857468    -2.76   0.006     -.404851   -.0677559
         pr3 |  -.3335426   .0845804    -3.94   0.000    -.4997974   -.1672879
        post |   .1374264   .0758957     1.81   0.071    -.0117573    .2866101
   post_miss |  -.1243318   .1263407    -0.98   0.326    -.3726724    .1240088
      health |   .1545399   .0649819     2.38   0.018     .0268086    .2822711
 health_miss |    .368892   .1856022     1.99   0.048     .0040645    .7337195
         sex |   .3793593   .0549319     6.91   0.000     .2713827    .4873358
         age |  -.0039619   .0022234    -1.78   0.075    -.0083323    .0004086
      single |   .0317926   .0675684     0.47   0.638    -.1010227    .1646078
       divor |   .4316679   .3144294     1.37   0.171    -.1863877    1.049724
     protest |  -.0569192   .0650675    -0.87   0.382    -.1848187    .0709803
         com |  -.0789702    .134904    -0.59   0.559     -.344143    .1862027
        prof |   .3686356   .1844414     2.00   0.046     .0060898    .7311814
         tea |   .6863623   .1126701     6.09   0.000     .4648932    .9078314
     comform |   .0003816   .2245174     0.00   0.999    -.4409392    .4417024
         dom |   .0403159   .0774992     0.52   0.603    -.1120197    .1926515
    econfood |  -.1015958   .0236641    -4.29   0.000    -.1481109   -.0550807
       house |   .1299587   .0748699     1.74   0.083    -.0172087    .2771262
      llomue |   .0664269   .1072246     0.62   0.536    -.1443382    .2771921
     chitsua |   .1797454    .249108     0.72   0.471    -.3099116    .6694024
      living |   .0029908    .025057     0.12   0.905    -.0462623    .0522438
       _cons |  -.0189457    .138982    -0.14   0.892    -.2921345    .2542432
------------------------------------------------------------------------------

Simultaneous results for zzscinfo_3_2a, zzscinfo_3_3a

                                                  Number of obs   =       1135

                                          (Std. Err. adjusted for 161 clusters in ea)
-------------------------------------------------------------------------------------
                    |               Robust
                    |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
zzscinfo_3_2a_mean  |
          civiceduc |   .1587572   .0622773     2.55   0.011     .0366959    .2808186
            hotline |   .1670904   .0556213     3.00   0.003     .0580746    .2761061
            verdade |   .1590748   .0636289     2.50   0.012     .0343645    .2837852
                pr1 |    -.37691   .0605221    -6.23   0.000    -.4955312   -.2582889
                pr2 |  -.2587417   .0650223    -3.98   0.000    -.3861831   -.1313002
                pr3 |  -.3558362   .0561054    -6.34   0.000    -.4658007   -.2458717
               post |   .0355766   .0797943     0.45   0.656    -.1208175    .1919706
          post_miss |  -.0684071   .1248703    -0.55   0.584    -.3131483    .1763341
             health |   .0969048   .0432238     2.24   0.025     .0121878    .1816218
        health_miss |   .1443601   .1298762     1.11   0.266    -.1101924    .3989127
                sex |   .3227034   .0356589     9.05   0.000     .2528133    .3925935
                age |   -.003377   .0015261    -2.21   0.027    -.0063681   -.0003858
             single |   -.035347   .0444294    -0.80   0.426    -.1224271    .0517331
              divor |   .0730867   .1761816     0.41   0.678    -.2722229    .4183962
            protest |  -.0538541   .0416229    -1.29   0.196    -.1354335    .0277252
                com |  -.0579435   .0942682    -0.61   0.539    -.2427058    .1268189
               prof |   .5533591   .1128735     4.90   0.000     .3321311    .7745872
                tea |   .5732219   .0584099     9.81   0.000     .4587405    .6877033
            comform |   .1569296   .1062583     1.48   0.140    -.0513328     .365192
                dom |   .0236669   .0592503     0.40   0.690    -.0924616    .1397953
           econfood |  -.0878316   .0158641    -5.54   0.000    -.1189247   -.0567386
              house |   .0896023   .0478566     1.87   0.061    -.0041949    .1833995
             llomue |   .0022977   .0846525     0.03   0.978    -.1636182    .1682135
            chitsua |   .2490166   .1519929     1.64   0.101    -.0488839    .5469172
             living |   .0029251   .0180781     0.16   0.871    -.0325072    .0383574
              _cons |    .114374   .1027415     1.11   0.266    -.0869956    .3157437
--------------------+----------------------------------------------------------------
zzscinfo_3_2a_lnvar |
              _cons |  -1.217553   .0493055   -24.69   0.000     -1.31419   -1.120916
--------------------+----------------------------------------------------------------
zzscinfo_3_3a_mean  |
          civiceduc |    .156073     .07914     1.97   0.049     .0009614    .3111847
            hotline |   .2536549   .0815399     3.11   0.002     .0938396    .4134701
            verdade |   .1738021   .0824986     2.11   0.035     .0121079    .3354963
                pr1 |  -.3608621   .0866242    -4.17   0.000    -.5306425   -.1910817
                pr2 |  -.2363035   .0927865    -2.55   0.011    -.4181616   -.0544453
                pr3 |  -.3335426   .0983782    -3.39   0.001    -.5263604   -.1407249
               post |   .1374264    .093547     1.47   0.142    -.0459223    .3207751
          post_miss |  -.1243318   .1803706    -0.69   0.491    -.4778517    .2291882
             health |   .1545399   .0625292     2.47   0.013     .0319848    .2770949
        health_miss |    .368892   .1545217     2.39   0.017      .066035    .6717489
                sex |   .3793593   .0438287     8.66   0.000     .2934565     .465262
                age |  -.0039619   .0024107    -1.64   0.100    -.0086867    .0007629
             single |   .0317926   .0587055     0.54   0.588     -.083268    .1468532
              divor |   .4316679   .1498606     2.88   0.004     .1379466    .7253893
            protest |  -.0569192   .0629807    -0.90   0.366     -.180359    .0665207
                com |  -.0789702   .1327227    -0.60   0.552    -.3391019    .1811615
               prof |   .3686356   .2427895     1.52   0.129    -.1072232    .8444943
                tea |   .6863623   .0623393    11.01   0.000     .5641796     .808545
            comform |   .0003816   .1487467     0.00   0.998    -.2911566    .2919198
                dom |   .0403159   .0856975     0.47   0.638     -.127648    .2082798
           econfood |  -.1015958   .0230357    -4.41   0.000     -.146745   -.0564466
              house |   .1299587   .0793707     1.64   0.102     -.025605    .2855224
             llomue |   .0664269   .1299636     0.51   0.609     -.188297    .3211509
            chitsua |   .1797454   .1441477     1.25   0.212    -.1027789    .4622697
             living |   .0029908   .0245011     0.12   0.903    -.0450304    .0510119
              _cons |  -.0189457   .1273608    -0.15   0.882    -.2685682    .2306768
--------------------+----------------------------------------------------------------
zzscinfo_3_3a_lnvar |
              _cons |  -1.254118   .0658696   -19.04   0.000     -1.38322   -1.125016
-------------------------------------------------------------------------------------

 ( 1)  [zzscinfo_3_2a_mean]civiceduc - [zzscinfo_3_3a_mean]civiceduc = 0

           chi2(  1) =    0.00
         Prob > chi2 =    0.9739
.973899

 ( 1)  [zzscinfo_3_2a_mean]hotline - [zzscinfo_3_3a_mean]hotline = 0

           chi2(  1) =    2.04
         Prob > chi2 =    0.1527
.15273446

 ( 1)  [zzscinfo_3_2a_mean]verdade - [zzscinfo_3_3a_mean]verdade = 0

           chi2(  1) =    0.04
         Prob > chi2 =    0.8335
.83348166

. 
. matrix define means=(m_zzscinfo_2_1, m_zzscinfo_3_1 \ t_zzscinfo_2_1_1, t_zzscinfo_3_1_1 \ t_z
> zscinfo_2_1_2, t_zzscinfo_3_1_2 \ t_zzscinfo_2_1_3, t_zzscinfo_3_1_3 \ t_zzscinfo_2_1_4, t_zzs
> cinfo_3_1_4 \ t_zzscinfo_2_5, t_zzscinfo_3_5 \ t_zzscinfo_2_6, t_zzscinfo_3_6 \ t_zzscinfo_2_7
> , t_zzscinfo_3_7)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_survey.xml") append sheet("info")
>  


note: results saved to outputregs_survey.xml

. xml_tab $list2, save("outputregs_survey.xml") append sheet("info stats") 


note: results saved to outputregs_survey.xml

. estimates clear

. 
. *trustcne
. 
. global final="zsctrustcne"

. 
. global list1=""

. global list2=""

. 
. foreach i in $final {
  2. 
.         regress `i' $treat $prov if time==1, cluster(ea)
  3.         estimates store `i'_2_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2_1=r(mean)
  6.         display m_`i'_2_1
  7.         test civiceduc = hotline
  8.         scalar define t_`i'_2_1_1=r(p)
  9.         display t_`i'_2_1_1
 10.         test civiceduc = verdade
 11.         scalar define t_`i'_2_1_2=r(p)
 12.         display t_`i'_2_1_2
 13.         test hotline = verdade
 14.         scalar define t_`i'_2_1_3=r(p)
 15.         display t_`i'_2_1_3
 16.         test civiceduc hotline verdade
 17.         scalar define t_`i'_2_1_4=r(p)
 18.         display t_`i'_2_1_4
 19. 
.         regress `i' $treat $prov if time==1 & lazy==0, cluster(ea)
 20.         estimates store `i'_2_2
 21.         regress `i' $treat $prov if time==1 & lazy==0
 22.         estimates store `i'_2_2a
 23.         
.         regress `i' $treat $prov if time==1 & (lazy==1|control==1), cluster(ea)
 24.         estimates store `i'_2_3
 25.         regress `i' $treat $prov if time==1 & (lazy==1|control==1)
 26.         estimates store `i'_2_3a
 27. 
.         suest `i'_2_2a `i'_2_3a, cluster(ea)
 28.         test [`i'_2_2a_mean]civiceduc=[`i'_2_3a_mean]civiceduc  
 29.         scalar define t_`i'_2_5=r(p)
 30.         display t_`i'_2_5
 31.         test [`i'_2_2a_mean]hotline=[`i'_2_3a_mean]hotline      
 32.         scalar define t_`i'_2_6=r(p)
 33.         display t_`i'_2_6
 34.         test [`i'_2_2a_mean]verdade=[`i'_2_3a_mean]verdade
 35.         scalar define t_`i'_2_7=r(p)
 36.         display t_`i'_2_7
 37. 
.         regress `i' $treat $prov $ea $controls if time==1, cluster(ea)
 38.         estimates store `i'_3_1
 39.         sum `i' if e(sample) & control == 1
 40.         scalar define m_`i'_3_1=r(mean)
 41.         display m_`i'_3_1
 42.         test civiceduc = hotline
 43.         scalar define t_`i'_3_1_1=r(p)
 44.         display t_`i'_3_1_1
 45.         test civiceduc = verdade
 46.         scalar define t_`i'_3_1_2=r(p)
 47.         display t_`i'_3_1_2
 48.         test hotline = verdade
 49.         scalar define t_`i'_3_1_3=r(p)
 50.         display t_`i'_3_1_3
 51.         test civiceduc hotline verdade
 52.         scalar define t_`i'_3_1_4=r(p)
 53.         display t_`i'_3_1_4
 54. 
.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0, cluster(ea)
 55.         estimates store `i'_3_2
 56.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0
 57.         estimates store `i'_3_2a
 58.         
.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1), cluster(ea)
 59.         estimates store `i'_3_3
 60.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1)
 61.         estimates store `i'_3_3a
 62. 
.         suest `i'_3_2a `i'_3_3a, cluster(ea)
 63.         test [`i'_3_2a_mean]civiceduc=[`i'_3_3a_mean]civiceduc  
 64.         scalar define t_`i'_3_5=r(p)
 65.         display t_`i'_3_5
 66.         test [`i'_3_2a_mean]hotline=[`i'_3_3a_mean]hotline      
 67.         scalar define t_`i'_3_6=r(p)
 68.         display t_`i'_3_6
 69.         test [`i'_3_2a_mean]verdade=[`i'_3_3a_mean]verdade
 70.         scalar define t_`i'_3_7=r(p)
 71.         display t_`i'_3_7
 72.         
.         global list1="$list1" + " `i'_2_1" + " `i'_3_1" + " `i'_2_2" + " `i'_3_2"  + " `i'_2_3
> " + " `i'_3_3"
 73.         
.         }

Linear regression                                      Number of obs =    1068
                                                       F(  6,   160) =   18.58
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0730
                                                       Root MSE      =  .86714

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 zsctrustcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .2042858   .0779489     2.62   0.010     .0503444    .3582273
     hotline |    .107156   .0811257     1.32   0.188    -.0530592    .2673712
     verdade |   .1737893   .0781872     2.22   0.028     .0193773    .3282014
         pr1 |  -.2949218   .0725203    -4.07   0.000    -.4381422   -.1517014
         pr2 |  -.6519762   .0791804    -8.23   0.000    -.8083496   -.4956027
         pr3 |  -.3469324    .057736    -6.01   0.000    -.4609554   -.2329094
       _cons |   .3130101   .0685106     4.57   0.000     .1777085    .4483118
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zsctrustcne |       262   -1.54e-09           1  -2.638136   .6376536
-1.536e-09

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    1.65
            Prob > F =    0.2005
.20049094

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.18
            Prob > F =    0.6741
.67407255

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.76
            Prob > F =    0.3834
.38342062

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    2.62
            Prob > F =    0.0525
.0524671

Linear regression                                      Number of obs =     907
                                                       F(  6,   160) =   14.75
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0754
                                                       Root MSE      =  .88157

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 zsctrustcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1895209   .0855612     2.22   0.028      .020546    .3584958
     hotline |   .1327306   .0865449     1.53   0.127     -.038187    .3036482
     verdade |   .1553502   .0835498     1.86   0.065    -.0096525    .3203528
         pr1 |  -.2602827   .0774655    -3.36   0.001    -.4132695   -.1072959
         pr2 |  -.6797147   .0911692    -7.46   0.000    -.8597649   -.4996646
         pr3 |  -.3137563    .069113    -4.54   0.000    -.4502477   -.1772649
       _cons |   .3016446   .0711281     4.24   0.000     .1611735    .4421156
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     907
-------------+------------------------------           F(  6,   900) =   12.23
       Model |  57.0355456     6  9.50592426           Prob > F      =  0.0000
    Residual |   699.44119   900  .777156878           R-squared     =  0.0754
-------------+------------------------------           Adj R-squared =  0.0692
       Total |  756.476736   906  .834963285           Root MSE      =  .88157

------------------------------------------------------------------------------
 zsctrustcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1895209   .0803269     2.36   0.019      .031871    .3471708
     hotline |   .1327306   .0811718     1.64   0.102    -.0265776    .2920387
     verdade |   .1553502   .0820347     1.89   0.059    -.0056514    .3163517
         pr1 |  -.2602827     .08037    -3.24   0.001    -.4180172   -.1025482
         pr2 |  -.6797147   .0838923    -8.10   0.000     -.844362   -.5150675
         pr3 |  -.3137563   .0821258    -3.82   0.000    -.4749367   -.1525759
       _cons |   .3016446   .0734827     4.10   0.000     .1574271     .445862
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     423
                                                       F(  6,   144) =    8.60
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0658
                                                       Root MSE      =  .90915

                                   (Std. Err. adjusted for 145 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 zsctrustcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .2746979    .112458     2.44   0.016     .0524162    .4969796
     hotline |   .0323486   .1247577     0.26   0.796    -.2142443    .2789416
     verdade |     .24443   .1215434     2.01   0.046     .0041902    .4846697
         pr1 |  -.2972756   .1217837    -2.44   0.016    -.5379901    -.056561
         pr2 |  -.5887622   .1125449    -5.23   0.000    -.8112157   -.3663087
         pr3 |  -.3851785   .1118051    -3.45   0.001    -.6061696   -.1641874
       _cons |   .3090383   .0864779     3.57   0.000     .1381082    .4799685
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     423
-------------+------------------------------           F(  6,   416) =    4.89
       Model |  24.2281457     6  4.03802428           Prob > F      =  0.0001
    Residual |  343.843858   416  .826547735           R-squared     =  0.0658
-------------+------------------------------           Adj R-squared =  0.0524
       Total |  368.072003   422  .872208538           Root MSE      =  .90915

------------------------------------------------------------------------------
 zsctrustcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .2746979   .1415933     1.94   0.053    -.0036296    .5530255
     hotline |   .0323486    .128688     0.25   0.802    -.2206113    .2853085
     verdade |     .24443   .1406323     1.74   0.083    -.0320086    .5208685
         pr1 |  -.2972756   .1210913    -2.45   0.014    -.5353026   -.0592485
         pr2 |  -.5887622   .1285811    -4.58   0.000    -.8415119   -.3360125
         pr3 |  -.3851785   .1221558    -3.15   0.002    -.6252982   -.1450589
       _cons |   .3090383   .0929808     3.32   0.001     .1262675    .4918092
------------------------------------------------------------------------------

Simultaneous results for zsctrustcne_2_2a, zsctrustcne_2_3a

                                                  Number of obs   =       1068

                                             (Std. Err. adjusted for 161 clusters in ea)
----------------------------------------------------------------------------------------
                       |               Robust
                       |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
zsctrustcne_2_2a_mean  |
             civiceduc |   .1895209   .0852774     2.22   0.026     .0223802    .3566616
               hotline |   .1327306   .0862578     1.54   0.124    -.0363317    .3017928
               verdade |   .1553502   .0832727     1.87   0.062    -.0078613    .3185616
                   pr1 |  -.2602827   .0772086    -3.37   0.001    -.4116087   -.1089567
                   pr2 |  -.6797147   .0908668    -7.48   0.000    -.8578104   -.5016191
                   pr3 |  -.3137563   .0688838    -4.55   0.000     -.448766   -.1787465
                 _cons |   .3016446   .0708922     4.25   0.000     .1626984    .4405907
-----------------------+----------------------------------------------------------------
zsctrustcne_2_2a_lnvar |
                 _cons |   -.252113   .0737393    -3.42   0.001    -.3966394   -.1075867
-----------------------+----------------------------------------------------------------
zsctrustcne_2_3a_mean  |
             civiceduc |   .2746979   .1116172     2.46   0.014     .0559323    .4934636
               hotline |   .0323486   .1238249     0.26   0.794    -.2103437     .275041
               verdade |     .24443   .1206347     2.03   0.043     .0079903    .4808696
                   pr1 |  -.2972756   .1208731    -2.46   0.014    -.5341825   -.0603686
                   pr2 |  -.5887622   .1117034    -5.27   0.000    -.8076969   -.3698275
                   pr3 |  -.3851785   .1109691    -3.47   0.001     -.602674   -.1676831
                 _cons |   .3090383   .0858313     3.60   0.000      .140812    .4772647
-----------------------+----------------------------------------------------------------
zsctrustcne_2_3a_lnvar |
                 _cons |  -.1904976   .0950214    -2.00   0.045     -.376736   -.0042592
----------------------------------------------------------------------------------------

 ( 1)  [zsctrustcne_2_2a_mean]civiceduc - [zsctrustcne_2_3a_mean]civiceduc = 0

           chi2(  1) =    0.49
         Prob > chi2 =    0.4817
.48171075

 ( 1)  [zsctrustcne_2_2a_mean]hotline - [zsctrustcne_2_3a_mean]hotline = 0

           chi2(  1) =    0.64
         Prob > chi2 =    0.4225
.42248075

 ( 1)  [zsctrustcne_2_2a_mean]verdade - [zsctrustcne_2_3a_mean]verdade = 0

           chi2(  1) =    0.52
         Prob > chi2 =    0.4728
.47277188

Linear regression                                      Number of obs =    1053
                                                       F( 25,   160) =    8.15
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1078
                                                       Root MSE      =  .86195

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 zsctrustcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1970892   .0768773     2.56   0.011     .0452641    .3489142
     hotline |   .1420105    .076924     1.85   0.067    -.0099068    .2939278
     verdade |    .170444   .0745718     2.29   0.024     .0231721    .3177159
         pr1 |  -.4403259   .0961358    -4.58   0.000    -.6301846   -.2504672
         pr2 |  -.7268473   .0881054    -8.25   0.000    -.9008469   -.5528477
         pr3 |   -.402037   .0750358    -5.36   0.000    -.5502254   -.2538486
        post |   .1393817    .093174     1.50   0.137    -.0446279    .3233912
   post_miss |  -.3672065   .1376653    -2.67   0.008    -.6390819   -.0953312
      health |  -.0966615   .0607528    -1.59   0.114    -.2166424    .0233193
 health_miss |  -.0073809   .2027948    -0.04   0.971    -.4078807     .393119
         sex |  -.1218411   .0540186    -2.26   0.025    -.2285226   -.0151597
         age |   .0022335   .0021549     1.04   0.302    -.0020222    .0064892
      single |  -.0194172   .0742267    -0.26   0.794    -.1660075    .1271731
       divor |    .190398   .1721314     1.11   0.270    -.1495446    .5303405
     protest |   .0616402   .0704397     0.88   0.383    -.0774713    .2007517
         com |     .09747   .1177151     0.83   0.409    -.1350057    .3299458
        prof |   -.129403   .2237584    -0.58   0.564    -.5713037    .3124978
         tea |    .179266   .0978933     1.83   0.069    -.0140636    .3725957
     comform |  -.4057265   .2871441    -1.41   0.160    -.9728078    .1613548
         dom |   .0461974   .0889599     0.52   0.604    -.1294896    .2218844
    econfood |  -.0352593   .0250475    -1.41   0.161    -.0847256    .0142071
       house |   .0331983   .0832711     0.40   0.691     -.131254    .1976506
      llomue |   .2243354   .1195473     1.88   0.062    -.0117587    .4604295
     chitsua |   -.642704   .2931774    -2.19   0.030    -1.221701   -.0637075
      living |   .0034364   .0278133     0.12   0.902    -.0514922     .058365
       _cons |   .3506575   .1393169     2.52   0.013     .0755202    .6257947
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zsctrustcne |       259    -.004224    1.004157  -2.638136   .6376536
-.00422399

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.53
            Prob > F =    0.4668
.4667879

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.14
            Prob > F =    0.7040
.70396196

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.15
            Prob > F =    0.7032
.70318904

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    2.55
            Prob > F =    0.0574
.05741259

Linear regression                                      Number of obs =     896
                                                       F( 25,   160) =    6.55
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1130
                                                       Root MSE      =  .87557

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 zsctrustcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .182225   .0813625     2.24   0.026     .0215421    .3429079
     hotline |   .1679185   .0820849     2.05   0.042     .0058088    .3300282
     verdade |   .1558146   .0821029     1.90   0.060    -.0063307    .3179598
         pr1 |  -.4060045   .1100952    -3.69   0.000    -.6234316   -.1885773
         pr2 |  -.7516061   .1005884    -7.47   0.000    -.9502583   -.5529539
         pr3 |  -.3710316   .0865592    -4.29   0.000    -.5419775   -.2000857
        post |   .1319159   .1084121     1.22   0.225    -.0821873    .3460192
   post_miss |  -.4015829   .1380619    -2.91   0.004    -.6742416   -.1289242
      health |  -.0669506   .0689411    -0.97   0.333    -.2031024    .0692013
 health_miss |    .027759   .2224124     0.12   0.901    -.4114836    .4670017
         sex |  -.1479033   .0565693    -2.61   0.010     -.259622   -.0361845
         age |   .0018718   .0023154     0.81   0.420    -.0027008    .0064444
      single |  -.0465155    .085242    -0.55   0.586    -.2148602    .1218291
       divor |   .4333861   .1364176     3.18   0.002     .1639748    .7027974
     protest |   .0432441   .0766883     0.56   0.574    -.1082077    .1946959
         com |   .1526909   .1215786     1.26   0.211    -.0874147    .3927966
        prof |  -.2173952   .2587718    -0.84   0.402    -.7284441    .2936536
         tea |   .1178639   .1201592     0.98   0.328    -.1194387    .3551666
     comform |  -.3410407   .2890185    -1.18   0.240    -.9118237    .2297423
         dom |   .0231425   .1024571     0.23   0.822    -.1792003    .2254852
    econfood |  -.0251915   .0270662    -0.93   0.353    -.0786446    .0282617
       house |    .045807   .0909269     0.50   0.615    -.1337647    .2253786
      llomue |   .2434191   .1270901     1.92   0.057    -.0075712    .4944095
     chitsua |  -.7136387   .2975957    -2.40   0.018    -1.301361   -.1259164
      living |   .0147972   .0301358     0.49   0.624     -.044718    .0743124
       _cons |   .3035716   .1564699     1.94   0.054    -.0054409    .6125842
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     896
-------------+------------------------------           F( 25,   870) =    4.43
       Model |  84.9469531    25  3.39787812           Prob > F      =  0.0000
    Residual |  666.956524   870  .766616694           R-squared     =  0.1130
-------------+------------------------------           Adj R-squared =  0.0875
       Total |  751.903477   895  .840115617           Root MSE      =  .87557

------------------------------------------------------------------------------
 zsctrustcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |    .182225   .0841536     2.17   0.031     .0170572    .3473927
     hotline |   .1679185   .0833299     2.02   0.044     .0043675    .3314696
     verdade |   .1558146   .0849627     1.83   0.067    -.0109413    .3225704
         pr1 |  -.4060045   .0996429    -4.07   0.000    -.6015731   -.2104358
         pr2 |  -.7516061   .0992435    -7.57   0.000    -.9463908   -.5568214
         pr3 |  -.3710316    .094758    -3.92   0.000    -.5570126   -.1850505
        post |   .1319159   .1014817     1.30   0.194    -.0672617    .3310936
   post_miss |  -.4015829   .1746402    -2.30   0.022    -.7443483   -.0588175
      health |  -.0669506   .0702961    -0.95   0.341    -.2049204    .0710193
 health_miss |    .027759   .1957073     0.14   0.887    -.3563546    .4118727
         sex |  -.1479033   .0627013    -2.36   0.019    -.2709668   -.0248397
         age |   .0018718   .0024323     0.77   0.442     -.002902    .0066457
      single |  -.0465155   .0814833    -0.57   0.568    -.2064423    .1134112
       divor |   .4333861   .3991117     1.09   0.278    -.3499482     1.21672
     protest |   .0432441     .07272     0.59   0.552    -.0994829    .1859712
         com |   .1526909   .1427689     1.07   0.285    -.1275208    .4329027
        prof |  -.2173952   .2316777    -0.94   0.348    -.6721077    .2373172
         tea |   .1178639   .1411903     0.83   0.404    -.1592496    .3949774
     comform |  -.3410407   .2709474    -1.26   0.208    -.8728276    .1907462
         dom |   .0231425   .0925037     0.25   0.803     -.158414     .204699
    econfood |  -.0251915   .0263062    -0.96   0.339    -.0768225    .0264396
       house |    .045807   .0856544     0.53   0.593    -.1223065    .2139205
      llomue |   .2434191    .116549     2.09   0.037     .0146691    .4721691
     chitsua |  -.7136387   .2861379    -2.49   0.013     -1.27524   -.1520375
      living |   .0147972   .0294307     0.50   0.615    -.0429662    .0725606
       _cons |   .3035716   .1607122     1.89   0.059    -.0118573    .6190006
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     416
                                                       F( 25,   144) =    6.09
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1140
                                                       Root MSE      =  .91228

                                   (Std. Err. adjusted for 145 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 zsctrustcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .2268099   .1259963     1.80   0.074    -.0222313     .475851
     hotline |   .0231852   .1269591     0.18   0.855    -.2277589    .2741293
     verdade |   .2023851   .1177117     1.72   0.088    -.0302808    .4350511
         pr1 |  -.4500756   .1875954    -2.40   0.018     -.820872   -.0792792
         pr2 |  -.7317874    .143763    -5.09   0.000    -1.015946    -.447629
         pr3 |  -.4430591   .1366733    -3.24   0.001    -.7132043    -.172914
        post |   .1492039   .1426551     1.05   0.297    -.1327646    .4311724
   post_miss |  -.3711278   .1503207    -2.47   0.015     -.668248   -.0740076
      health |  -.2234667   .1022657    -2.19   0.030    -.4256026   -.0213307
 health_miss |   .1276559   .2449878     0.52   0.603    -.3565808    .6118926
         sex |  -.1252563    .098284    -1.27   0.205     -.319522    .0690094
         age |   .0070443   .0040715     1.73   0.086    -.0010033    .0150919
      single |   .0399074   .1173028     0.34   0.734    -.1919505    .2717652
       divor |  -.0054254   .3258995    -0.02   0.987    -.6495902    .6387394
     protest |   .1133502   .1214464     0.93   0.352    -.1266977    .3533982
         com |  -.1817105   .2090222    -0.87   0.386    -.5948587    .2314376
        prof |   .1919373   .2633861     0.73   0.467    -.3286651    .7125398
         tea |   .1219833   .2005511     0.61   0.544     -.274421    .5183875
     comform |  -.6880523   .4546148    -1.51   0.132    -1.586633    .2105279
         dom |  -.0981682   .1838638    -0.53   0.594    -.4615888    .2652524
    econfood |   -.028905   .0416209    -0.69   0.488    -.1111719    .0533619
       house |  -.0768696   .1296129    -0.59   0.554    -.3330593      .17932
      llomue |   .1637094   .2160538     0.76   0.450    -.2633371    .5907559
     chitsua |  -.2291371   .3780797    -0.61   0.545    -.9764399    .5181658
      living |   .0028471   .0438211     0.06   0.948    -.0837686    .0894628
       _cons |   .3803133   .2241773     1.70   0.092      -.06279    .8234166
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     416
-------------+------------------------------           F( 25,   390) =    2.01
       Model |  41.7650138    25  1.67060055           Prob > F      =  0.0032
    Residual |  324.576551   390  .832247567           R-squared     =  0.1140
-------------+------------------------------           Adj R-squared =  0.0572
       Total |  366.341565   415  .882750759           Root MSE      =  .91228

------------------------------------------------------------------------------
 zsctrustcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .2268099   .1494977     1.52   0.130    -.0671123    .5207321
     hotline |   .0231852   .1361611     0.17   0.865    -.2445165    .2908868
     verdade |   .2023851   .1500163     1.35   0.178    -.0925566    .4973269
         pr1 |  -.4500756   .1558104    -2.89   0.004     -.756409   -.1437422
         pr2 |  -.7317874   .1549622    -4.72   0.000    -1.036453   -.4271216
         pr3 |  -.4430591   .1495628    -2.96   0.003    -.7371094   -.1490089
        post |   .1492039   .1359215     1.10   0.273    -.1180266    .4164344
   post_miss |  -.3711278   .2275135    -1.63   0.104    -.8184342    .0761785
      health |  -.2234667   .1149937    -1.94   0.053    -.4495517    .0026184
 health_miss |   .1276559   .3373026     0.38   0.705    -.5355031    .7908149
         sex |  -.1252563   .0959824    -1.30   0.193    -.3139641    .0634514
         age |   .0070443    .003916     1.80   0.073    -.0006549    .0147435
      single |   .0399074   .1195332     0.33   0.739    -.1951028    .2749175
       divor |  -.0054254   .5373651    -0.01   0.992     -1.06192    1.051069
     protest |   .1133502   .1180374     0.96   0.338    -.1187189    .3454194
         com |  -.1817105    .237632    -0.76   0.445    -.6489105    .2854894
        prof |   .1919373   .3154407     0.61   0.543    -.4282396    .8121143
         tea |   .1219833    .193045     0.63   0.528    -.2575559    .5015224
     comform |  -.6880523    .384563    -1.79   0.074    -1.444128    .0680237
         dom |  -.0981682   .1404768    -0.70   0.485    -.3743547    .1780183
    econfood |   -.028905    .042398    -0.68   0.496    -.1122623    .0544523
       house |  -.0768696   .1364909    -0.56   0.574    -.3452196    .1914803
      llomue |   .1637094    .184929     0.89   0.377    -.1998731     .527292
     chitsua |  -.2291371   .4767095    -0.48   0.631    -1.166379     .708105
      living |   .0028471   .0440748     0.06   0.949    -.0838069     .089501
       _cons |   .3803133   .2440722     1.56   0.120    -.0995486    .8601752
------------------------------------------------------------------------------

Simultaneous results for zsctrustcne_3_2a, zsctrustcne_3_3a

                                                  Number of obs   =       1053

                                             (Std. Err. adjusted for 161 clusters in ea)
----------------------------------------------------------------------------------------
                       |               Robust
                       |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
zsctrustcne_3_2a_mean  |
             civiceduc |    .182225   .0802181     2.27   0.023     .0250005    .3394495
               hotline |   .1679185   .0809304     2.07   0.038     .0092979    .3265392
               verdade |   .1558146   .0809481     1.92   0.054    -.0028409      .31447
                   pr1 |  -.4060045   .1085467    -3.74   0.000     -.618752    -.193257
                   pr2 |  -.7516061   .0991736    -7.58   0.000    -.9459828   -.5572294
                   pr3 |  -.3710316   .0853417    -4.35   0.000    -.5382983   -.2037649
                  post |   .1319159   .1068872     1.23   0.217    -.0775792    .3414111
             post_miss |  -.4015829     .13612    -2.95   0.003    -.6683732   -.1347926
                health |  -.0669506   .0679714    -0.98   0.325    -.2001721    .0662709
           health_miss |    .027759   .2192841     0.13   0.899    -.4020299     .457548
                   sex |  -.1479033   .0557736    -2.65   0.008    -.2572175    -.038589
                   age |   .0018718   .0022828     0.82   0.412    -.0026023     .006346
                single |  -.0465155   .0840431    -0.55   0.580    -.2112369    .1182058
                 divor |   .4333861   .1344988     3.22   0.001     .1697733     .696999
               protest |   .0432441   .0756096     0.57   0.567     -.104948    .1914363
                   com |   .1526909   .1198685     1.27   0.203     -.082247    .3876289
                  prof |  -.2173952   .2551321    -0.85   0.394    -.7174449    .2826544
                   tea |   .1178639   .1184692     0.99   0.320    -.1143313    .3500592
               comform |  -.3410407   .2849533    -1.20   0.231    -.8995389    .2174575
                   dom |   .0231425    .101016     0.23   0.819    -.1748454    .2211303
              econfood |  -.0251915   .0266855    -0.94   0.345    -.0774941    .0271112
                 house |    .045807    .089648     0.51   0.609    -.1298998    .2215138
                llomue |   .2434191   .1253025     1.94   0.052    -.0021692    .4890075
               chitsua |  -.7136387   .2934099    -2.43   0.015    -1.288712   -.1385658
                living |   .0147972   .0297119     0.50   0.618     -.043437    .0730315
                 _cons |   .3035716    .154269     1.97   0.049     .0012098    .6059334
-----------------------+----------------------------------------------------------------
zsctrustcne_3_2a_lnvar |
                 _cons |  -.2657683   .0715436    -3.71   0.000    -.4059912   -.1255454
-----------------------+----------------------------------------------------------------
zsctrustcne_3_3a_mean  |
             civiceduc |   .2268099   .1221002     1.86   0.063     -.012502    .4661218
               hotline |   .0231852   .1230331     0.19   0.851    -.2179554    .2643257
               verdade |   .2023851   .1140717     1.77   0.076    -.0211913    .4259616
                   pr1 |  -.4500756   .1817945    -2.48   0.013    -.8063862    -.093765
                   pr2 |  -.7317874   .1393175    -5.25   0.000    -1.004845   -.4587301
                   pr3 |  -.4430591    .132447    -3.35   0.001    -.7026506   -.1834677
                  post |   .1492039   .1382438     1.08   0.280     -.121749    .4201568
             post_miss |  -.3711278   .1456724    -2.55   0.011    -.6566405   -.0856151
                health |  -.2234667   .0991034    -2.25   0.024    -.4177058   -.0292275
           health_miss |   .1276559   .2374121     0.54   0.591    -.3376633     .592975
                   sex |  -.1252563   .0952448    -1.32   0.188    -.3119327      .06142
                   age |   .0070443   .0039456     1.79   0.074    -.0006889    .0147775
                single |   .0399074   .1136755     0.35   0.726    -.1828925    .2627073
                 divor |  -.0054254   .3158218    -0.02   0.986    -.6244248     .613574
               protest |   .1133502    .117691     0.96   0.335    -.1173198    .3440203
                   com |  -.1817105   .2025587    -0.90   0.370    -.5787183    .2152973
                  prof |   .1919373   .2552415     0.75   0.452    -.3083268    .6922015
                   tea |   .1219833   .1943495     0.63   0.530    -.2589348    .5029013
               comform |  -.6880523   .4405569    -1.56   0.118    -1.551528    .1754233
                   dom |  -.0981682   .1781782    -0.55   0.582    -.4473911    .2510547
              econfood |   -.028905   .0403339    -0.72   0.474     -.107958     .050148
                 house |  -.0768696   .1256049    -0.61   0.541    -.3230508    .1693115
                llomue |   .1637094   .2093728     0.78   0.434    -.2466538    .5740726
               chitsua |  -.2291371   .3663885    -0.63   0.532    -.9472453    .4889711
                living |   .0028471    .042466     0.07   0.947    -.0803848     .086079
                 _cons |   .3803133   .2172452     1.75   0.080    -.0454794     .806106
-----------------------+----------------------------------------------------------------
zsctrustcne_3_3a_lnvar |
                 _cons |  -.1836253   .0927548    -1.98   0.048    -.3654214   -.0018293
----------------------------------------------------------------------------------------

 ( 1)  [zsctrustcne_3_2a_mean]civiceduc - [zsctrustcne_3_3a_mean]civiceduc = 0

           chi2(  1) =    0.13
         Prob > chi2 =    0.7217
.72167555

 ( 1)  [zsctrustcne_3_2a_mean]hotline - [zsctrustcne_3_3a_mean]hotline = 0

           chi2(  1) =    1.32
         Prob > chi2 =    0.2503
.25034497

 ( 1)  [zsctrustcne_3_2a_mean]verdade - [zsctrustcne_3_3a_mean]verdade = 0

           chi2(  1) =    0.15
         Prob > chi2 =    0.6982
.69823088

. 
. matrix define means=(m_zsctrustcne_2_1, m_zsctrustcne_3_1 \ t_zsctrustcne_2_1_1, t_zsctrustcne
> _3_1_1 \ t_zsctrustcne_2_1_2, t_zsctrustcne_3_1_2 \ t_zsctrustcne_2_1_3, t_zsctrustcne_3_1_3 \
>  t_zsctrustcne_2_1_4, t_zsctrustcne_3_1_4 \ t_zsctrustcne_2_5, t_zsctrustcne_3_5 \ t_zsctrustc
> ne_2_6, t_zsctrustcne_3_6 \ t_zsctrustcne_2_7, t_zsctrustcne_3_7)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_survey.xml") append sheet("trustc
> ne") 


note: results saved to outputregs_survey.xml

. xml_tab $list2, save("outputregs_survey.xml") append sheet("trustcne stats") 


note: results saved to outputregs_survey.xml

. estimates clear

. 
. *indepcne
. 
. global final="zscindepcne"

. 
. global list1=""

. global list2=""

. 
. foreach i in $final {
  2. 
.         regress `i' $treat $prov if time==1, cluster(ea)
  3.         estimates store `i'_2_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2_1=r(mean)
  6.         display m_`i'_2_1
  7.         test civiceduc = hotline
  8.         scalar define t_`i'_2_1_1=r(p)
  9.         display t_`i'_2_1_1
 10.         test civiceduc = verdade
 11.         scalar define t_`i'_2_1_2=r(p)
 12.         display t_`i'_2_1_2
 13.         test hotline = verdade
 14.         scalar define t_`i'_2_1_3=r(p)
 15.         display t_`i'_2_1_3
 16.         test civiceduc hotline verdade
 17.         scalar define t_`i'_2_1_4=r(p)
 18.         display t_`i'_2_1_4
 19. 
.         regress `i' $treat $prov if time==1 & lazy==0, cluster(ea)
 20.         estimates store `i'_2_2
 21.         regress `i' $treat $prov if time==1 & lazy==0
 22.         estimates store `i'_2_2a
 23.         
.         regress `i' $treat $prov if time==1 & (lazy==1|control==1), cluster(ea)
 24.         estimates store `i'_2_3
 25.         regress `i' $treat $prov if time==1 & (lazy==1|control==1)
 26.         estimates store `i'_2_3a
 27. 
.         suest `i'_2_2a `i'_2_3a, cluster(ea)
 28.         test [`i'_2_2a_mean]civiceduc=[`i'_2_3a_mean]civiceduc  
 29.         scalar define t_`i'_2_5=r(p)
 30.         display t_`i'_2_5
 31.         test [`i'_2_2a_mean]hotline=[`i'_2_3a_mean]hotline      
 32.         scalar define t_`i'_2_6=r(p)
 33.         display t_`i'_2_6
 34.         test [`i'_2_2a_mean]verdade=[`i'_2_3a_mean]verdade
 35.         scalar define t_`i'_2_7=r(p)
 36.         display t_`i'_2_7
 37. 
.         regress `i' $treat $prov $ea $controls if time==1, cluster(ea)
 38.         estimates store `i'_3_1
 39.         sum `i' if e(sample) & control == 1
 40.         scalar define m_`i'_3_1=r(mean)
 41.         display m_`i'_3_1
 42.         test civiceduc = hotline
 43.         scalar define t_`i'_3_1_1=r(p)
 44.         display t_`i'_3_1_1
 45.         test civiceduc = verdade
 46.         scalar define t_`i'_3_1_2=r(p)
 47.         display t_`i'_3_1_2
 48.         test hotline = verdade
 49.         scalar define t_`i'_3_1_3=r(p)
 50.         display t_`i'_3_1_3
 51.         test civiceduc hotline verdade
 52.         scalar define t_`i'_3_1_4=r(p)
 53.         display t_`i'_3_1_4
 54. 
.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0, cluster(ea)
 55.         estimates store `i'_3_2
 56.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0
 57.         estimates store `i'_3_2a
 58.         
.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1), cluster(ea)
 59.         estimates store `i'_3_3
 60.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1)
 61.         estimates store `i'_3_3a
 62. 
.         suest `i'_3_2a `i'_3_3a, cluster(ea)
 63.         test [`i'_3_2a_mean]civiceduc=[`i'_3_3a_mean]civiceduc  
 64.         scalar define t_`i'_3_5=r(p)
 65.         display t_`i'_3_5
 66.         test [`i'_3_2a_mean]hotline=[`i'_3_3a_mean]hotline      
 67.         scalar define t_`i'_3_6=r(p)
 68.         display t_`i'_3_6
 69.         test [`i'_3_2a_mean]verdade=[`i'_3_3a_mean]verdade
 70.         scalar define t_`i'_3_7=r(p)
 71.         display t_`i'_3_7
 72.         
.         global list1="$list1" + " `i'_2_1" + " `i'_3_1" + " `i'_2_2" + " `i'_3_2"  + " `i'_2_3
> " + " `i'_3_3"
 73.         
.         }

Linear regression                                      Number of obs =    1033
                                                       F(  6,   160) =   21.13
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0898
                                                       Root MSE      =  .87353

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 zscindepcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1385467   .0862746     1.61   0.110    -.0318372    .3089306
     hotline |   .1697059   .0849275     2.00   0.047     .0019824    .3374293
     verdade |   .1513172    .085948     1.76   0.080    -.0184216     .321056
         pr1 |    -.33149   .0664789    -4.99   0.000    -.4627793   -.2002007
         pr2 |  -.7292679   .0872151    -8.36   0.000     -.901509   -.5570267
         pr3 |  -.4929722   .0712732    -6.92   0.000    -.6337298   -.3522147
       _cons |    .375802   .0719175     5.23   0.000     .2337719     .517832
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscindepcne |       258    1.77e-07    .9999999  -2.144556   .6819854
1.773e-07

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.16
            Prob > F =    0.6929
.6928949

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.03
            Prob > F =    0.8712
.87115726

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.06
            Prob > F =    0.8135
.81346718

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.54
            Prob > F =    0.2064
.20642552

Linear regression                                      Number of obs =     878
                                                       F(  6,   159) =   17.41
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0912
                                                       Root MSE      =  .89114

                                   (Std. Err. adjusted for 160 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 zscindepcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1274896   .0909322     1.40   0.163    -.0521012    .3070803
     hotline |   .1689951   .0846941     2.00   0.048     .0017246    .3362657
     verdade |   .1405803   .0879574     1.60   0.112    -.0331351    .3142958
         pr1 |  -.3256551   .0732244    -4.45   0.000    -.4702729   -.1810373
         pr2 |  -.7648439   .0922523    -8.29   0.000    -.9470419   -.5826459
         pr3 |  -.4606142   .0751301    -6.13   0.000    -.6089959   -.3122325
       _cons |   .3744757   .0736751     5.08   0.000     .2289677    .5199837
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     878
-------------+------------------------------           F(  6,   871) =   14.58
       Model |  69.4513171     6  11.5752195           Prob > F      =  0.0000
    Residual |  691.685317   871  .794127804           R-squared     =  0.0912
-------------+------------------------------           Adj R-squared =  0.0850
       Total |  761.136635   877  .867886698           Root MSE      =  .89114

------------------------------------------------------------------------------
 zscindepcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1274896   .0825362     1.54   0.123    -.0345036    .2894827
     hotline |   .1689951    .082855     2.04   0.042     .0063764    .3316138
     verdade |   .1405803   .0843921     1.67   0.096    -.0250553     .306216
         pr1 |  -.3256551    .081568    -3.99   0.000     -.485748   -.1655622
         pr2 |  -.7648439   .0865232    -8.84   0.000    -.9346623   -.5950255
         pr3 |  -.4606142   .0841416    -5.47   0.000    -.6257583   -.2954702
       _cons |   .3744757   .0746246     5.02   0.000     .2280106    .5209408
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     413
                                                       F(  6,   141) =    8.01
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0852
                                                       Root MSE      =  .89965

                                   (Std. Err. adjusted for 142 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 zscindepcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1843418    .131831     1.40   0.164     -.076279    .4449627
     hotline |   .1803593   .1261579     1.43   0.155    -.0690462    .4297649
     verdade |   .1971328    .133891     1.47   0.143    -.0675606    .4618262
         pr1 |  -.2639374   .1274435    -2.07   0.040    -.5158844   -.0119904
         pr2 |  -.6782223   .1348046    -5.03   0.000    -.9447218   -.4117227
         pr3 |  -.4914872    .113333    -4.34   0.000    -.7155389   -.2674356
       _cons |   .3456998   .0867365     3.99   0.000     .1742277    .5171718
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     413
-------------+------------------------------           F(  6,   406) =    6.30
       Model |  30.6112655     6  5.10187758           Prob > F      =  0.0000
    Residual |   328.60416   406  .809369852           R-squared     =  0.0852
-------------+------------------------------           Adj R-squared =  0.0717
       Total |  359.215426   412  .871882101           Root MSE      =  .89965

------------------------------------------------------------------------------
 zscindepcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1843418   .1392759     1.32   0.186    -.0894501    .4581338
     hotline |   .1803593   .1293242     1.39   0.164    -.0738692    .4345879
     verdade |   .1971328   .1459863     1.35   0.178    -.0898505    .4841161
         pr1 |  -.2639374   .1203797    -2.19   0.029    -.5005828    -.027292
         pr2 |  -.6782223    .129723    -5.23   0.000    -.9332349   -.4232097
         pr3 |  -.4914872   .1219372    -4.03   0.000    -.7311944   -.2517801
       _cons |   .3456998   .0923438     3.74   0.000      .164168    .5272315
------------------------------------------------------------------------------

Simultaneous results for zscindepcne_2_2a, zscindepcne_2_3a

                                                  Number of obs   =       1033

                                             (Std. Err. adjusted for 161 clusters in ea)
----------------------------------------------------------------------------------------
                       |               Robust
                       |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
zscindepcne_2_2a_mean  |
             civiceduc |   .1274896   .0906188     1.41   0.159    -.0501201    .3050992
               hotline |   .1689951   .0844022     2.00   0.045     .0035698    .3344205
               verdade |   .1405803   .0876543     1.60   0.109    -.0312188    .3123795
                   pr1 |  -.3256551    .072972    -4.46   0.000    -.4686776   -.1826326
                   pr2 |  -.7648439   .0919344    -8.32   0.000     -.945032   -.5846557
                   pr3 |  -.4606142   .0748712    -6.15   0.000    -.6073591   -.3138694
                 _cons |   .3744757   .0734212     5.10   0.000     .2305729    .5183786
-----------------------+----------------------------------------------------------------
zscindepcne_2_2a_lnvar |
                 _cons |  -.2305109   .0628482    -3.67   0.000    -.3536911   -.1073306
-----------------------+----------------------------------------------------------------
zscindepcne_2_3a_mean  |
             civiceduc |   .1843418   .1308128     1.41   0.159    -.0720466    .4407303
               hotline |   .1803593   .1251836     1.44   0.150    -.0649959    .4257146
               verdade |   .1971328   .1328569     1.48   0.138     -.063262    .4575276
                   pr1 |  -.2639374   .1264592    -2.09   0.037    -.5117928    -.016082
                   pr2 |  -.6782223   .1337635    -5.07   0.000    -.9403939   -.4160506
                   pr3 |  -.4914872   .1124577    -4.37   0.000    -.7119003   -.2710742
                 _cons |   .3456998   .0860666     4.02   0.000     .1770123    .5143872
-----------------------+----------------------------------------------------------------
zscindepcne_2_3a_lnvar |
                 _cons |  -.2114993   .0861259    -2.46   0.014    -.3803029   -.0426957
----------------------------------------------------------------------------------------

 ( 1)  [zscindepcne_2_2a_mean]civiceduc - [zscindepcne_2_3a_mean]civiceduc = 0

           chi2(  1) =    0.20
         Prob > chi2 =    0.6548
.65479125

 ( 1)  [zscindepcne_2_2a_mean]hotline - [zscindepcne_2_3a_mean]hotline = 0

           chi2(  1) =    0.01
         Prob > chi2 =    0.9139
.91387524

 ( 1)  [zscindepcne_2_2a_mean]verdade - [zscindepcne_2_3a_mean]verdade = 0

           chi2(  1) =    0.21
         Prob > chi2 =    0.6438
.6437583

Linear regression                                      Number of obs =    1020
                                                       F( 25,   160) =    9.09
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1141
                                                       Root MSE      =  .87337

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 zscindepcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1156495   .0856631     1.35   0.179    -.0535267    .2848256
     hotline |   .1782503   .0782864     2.28   0.024     .0236423    .3328583
     verdade |   .1289162   .0858539     1.50   0.135    -.0406367    .2984691
         pr1 |  -.4104713   .0839073    -4.89   0.000      -.57618   -.2447626
         pr2 |  -.7533935   .0977253    -7.71   0.000    -.9463914   -.5603956
         pr3 |  -.4725488   .0944738    -5.00   0.000    -.6591252   -.2859724
        post |   .0809697   .1051047     0.77   0.442    -.1266018    .2885412
   post_miss |  -.2650659   .1345977    -1.97   0.051    -.5308831    .0007512
      health |   -.151667   .0710408    -2.13   0.034    -.2919656   -.0113685
 health_miss |   .0576906   .2294599     0.25   0.802      -.39547    .5108512
         sex |  -.0874794   .0578895    -1.51   0.133    -.2018054    .0268467
         age |    .004111   .0025646     1.60   0.111    -.0009539    .0091759
      single |     .07954   .0693605     1.15   0.253    -.0574401    .2165201
       divor |   .2767389    .181316     1.53   0.129    -.0813423    .6348201
     protest |  -.0155808   .0744776    -0.21   0.835    -.1626668    .1315051
         com |   .1599235    .122461     1.31   0.193    -.0819249    .4017719
        prof |  -.0506266   .2394672    -0.21   0.833    -.5235507    .4222976
         tea |   .0873294    .124845     0.70   0.485    -.1592272    .3338859
     comform |   -.050378   .2139738    -0.24   0.814    -.4729553    .3721992
         dom |   .0229378   .0827041     0.28   0.782    -.1403946    .1862703
    econfood |  -.0419133   .0252584    -1.66   0.099    -.0917961    .0079695
       house |   .0958447   .0862402     1.11   0.268    -.0744711    .2661605
      llomue |    .155652   .1185793     1.31   0.191    -.0785305    .3898345
     chitsua |   -.320685   .2083115    -1.54   0.126    -.7320797    .0907098
      living |   .0197088   .0307653     0.64   0.523    -.0410496    .0804671
       _cons |    .239927   .1769106     1.36   0.177     -.109454    .5893079
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 zscindepcne |       256    .0001928    1.001959  -2.144556   .6819854
.00019276

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.60
            Prob > F =    0.4385
.43853216

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.03
            Prob > F =    0.8721
.87210196

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.38
            Prob > F =    0.5386
.5385786

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.77
            Prob > F =    0.1548
.15476299

Linear regression                                      Number of obs =     869
                                                       F( 25,   159) =    7.86
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1180
                                                       Root MSE      =  .89038

                                   (Std. Err. adjusted for 160 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 zscindepcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1019365   .0903217     1.13   0.261    -.0764485    .2803215
     hotline |    .167699   .0781263     2.15   0.033     .0133999    .3219981
     verdade |   .1189081   .0906467     1.31   0.191    -.0601186    .2979349
         pr1 |  -.3925323   .0930148    -4.22   0.000    -.5762361   -.2088284
         pr2 |  -.8033415   .1063383    -7.55   0.000    -1.013359   -.5933237
         pr3 |  -.4640241   .1020234    -4.55   0.000    -.6655199   -.2625284
        post |   .0615132   .1154301     0.53   0.595    -.1664609    .2894872
   post_miss |  -.2697882   .1370103    -1.97   0.051    -.5403831    .0008066
      health |  -.1367187   .0743514    -1.84   0.068    -.2835625    .0101252
 health_miss |   .1298911   .2300139     0.56   0.573    -.3243855    .5841678
         sex |   -.095533   .0608127    -1.57   0.118     -.215638    .0245719
         age |   .0033986   .0026949     1.26   0.209    -.0019239    .0087211
      single |   .0441265    .080866     0.55   0.586    -.1155835    .2038365
       divor |   .5263727    .148067     3.55   0.000      .233941    .8188044
     protest |   .0089152    .084403     0.11   0.916    -.1577804    .1756109
         com |   .1735437    .118163     1.47   0.144    -.0598278    .4069152
        prof |  -.1376085   .2867874    -0.48   0.632    -.7040126    .4287956
         tea |   .0662102   .1458056     0.45   0.650    -.2217553    .3541758
     comform |  -.1084046   .2381517    -0.46   0.650    -.5787532    .3619441
         dom |   .0028298   .0932113     0.03   0.976    -.1812623    .1869218
    econfood |  -.0464355    .027928    -1.66   0.098    -.1015932    .0087221
       house |    .109221   .0935909     1.17   0.245    -.0756206    .2940626
      llomue |   .1463054   .1222112     1.20   0.233    -.0950612     .387672
     chitsua |  -.2374723   .2425763    -0.98   0.329    -.7165596     .241615
      living |   .0432618   .0346047     1.25   0.213    -.0250824    .1116059
       _cons |   .1890431    .183277     1.03   0.304    -.1729283    .5510144
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     869
-------------+------------------------------           F( 25,   843) =    4.51
       Model |  89.4369725    25   3.5774789           Prob > F      =  0.0000
    Residual |  668.312208   843   .79277842           R-squared     =  0.1180
-------------+------------------------------           Adj R-squared =  0.0919
       Total |  757.749181   868  .872982927           Root MSE      =  .89038

------------------------------------------------------------------------------
 zscindepcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1019365   .0867526     1.18   0.240    -.0683398    .2722129
     hotline |    .167699   .0855561     1.96   0.050     -.000229    .3356271
     verdade |   .1189081   .0876534     1.36   0.175    -.0531365    .2909528
         pr1 |  -.3925323   .1016825    -3.86   0.000    -.5921129   -.1929517
         pr2 |  -.8033415   .1037813    -7.74   0.000    -1.007042   -.5996414
         pr3 |  -.4640241   .0981542    -4.73   0.000    -.6566794   -.2713689
        post |   .0615132   .1059493     0.58   0.562    -.1464423    .2694686
   post_miss |  -.2697882   .1806032    -1.49   0.136     -.624273    .0846965
      health |  -.1367187   .0728069    -1.88   0.061    -.2796227    .0061854
 health_miss |   .1298911   .1998748     0.65   0.516    -.2624195    .5222018
         sex |   -.095533   .0645828    -1.48   0.139     -.222295     .031229
         age |   .0033986   .0025168     1.35   0.177    -.0015414    .0083386
      single |   .0441265   .0834952     0.53   0.597    -.1197565    .2080094
       divor |   .5263727     .40613     1.30   0.195    -.2707719    1.323517
     protest |   .0089152   .0758796     0.12   0.906    -.1400199    .1578503
         com |   .1735437   .1471653     1.18   0.239    -.1153097    .4623971
        prof |  -.1376085   .2443468    -0.56   0.573    -.6172081     .341991
         tea |   .0662102   .1451975     0.46   0.649    -.2187809    .3512014
     comform |  -.1084046   .2757782    -0.39   0.694    -.6496971     .432888
         dom |   .0028298   .0956185     0.03   0.976    -.1848484     .190508
    econfood |  -.0464355   .0272676    -1.70   0.089    -.0999559    .0070848
       house |    .109221   .0896902     1.22   0.224    -.0668213    .2852633
      llomue |   .1463054   .1193754     1.23   0.221    -.0880025    .3806134
     chitsua |  -.2374723   .3065426    -0.77   0.439    -.8391485     .364204
      living |   .0432618     .03031     1.43   0.154    -.0162301    .1027536
       _cons |   .1890431   .1665114     1.14   0.257    -.1377824    .5158686
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     407
                                                       F( 25,   141) =   11.54
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1460
                                                       Root MSE      =  .89465

                                   (Std. Err. adjusted for 142 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 zscindepcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1176178   .1441935     0.82   0.416    -.1674428    .4026785
     hotline |   .1894135   .1265985     1.50   0.137    -.0608631      .43969
     verdade |   .1334492   .1217801     1.10   0.275    -.1073018    .3742001
         pr1 |  -.4514499   .1493763    -3.02   0.003    -.7467567   -.1561432
         pr2 |  -.7670641   .1576022    -4.87   0.000    -1.078633   -.4554953
         pr3 |  -.4619451   .1334111    -3.46   0.001    -.7256897   -.1982004
        post |   .1082279   .1315981     0.82   0.412    -.1519326    .3683884
   post_miss |  -.1997235   .1200873    -1.66   0.099    -.4371279    .0376809
      health |  -.2732482   .1197557    -2.28   0.024     -.509997   -.0364994
 health_miss |  -.0655562   .2583674    -0.25   0.800    -.5763307    .4452184
         sex |  -.1119143    .095041    -1.18   0.241    -.2998038    .0759753
         age |   .0100979    .004182     2.41   0.017     .0018304    .0183653
      single |   .2036038   .0940487     2.16   0.032      .017676    .3895316
       divor |    .012505   .3156968     0.04   0.968     -.611606     .636616
     protest |   .0388731   .1231396     0.32   0.753    -.2045654    .2823116
         com |   .0302524   .2177743     0.14   0.890    -.4002724    .4607772
        prof |   .4361661   .1120017     3.89   0.000     .2147464    .6575858
         tea |   .0554109   .2295659     0.24   0.810    -.3984252    .5092469
     comform |  -.1836066   .2764119    -0.66   0.508    -.7300538    .3628407
         dom |   .0224803   .1404918     0.16   0.873    -.2552623     .300223
    econfood |  -.0591245   .0434117    -1.36   0.175    -.1449466    .0266975
       house |   .0548452    .128557     0.43   0.670    -.1993033    .3089936
      llomue |   .2846217   .2044348     1.39   0.166    -.1195319    .6887752
     chitsua |  -.4118774   .2155775    -1.91   0.058    -.8380594    .0143047
      living |   -.012027   .0475771    -0.25   0.801    -.1060835    .0820296
       _cons |   .2168672   .2651155     0.82   0.415     -.307248    .7409823
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     407
-------------+------------------------------           F( 25,   381) =    2.61
       Model |  52.1464737    25  2.08585895           Prob > F      =  0.0001
    Residual |  304.954255   381  .800404868           R-squared     =  0.1460
-------------+------------------------------           Adj R-squared =  0.0900
       Total |  357.100728   406  .879558445           Root MSE      =  .89465

------------------------------------------------------------------------------
 zscindepcne |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .1176178   .1452126     0.81   0.418    -.1679005    .4031362
     hotline |   .1894135   .1359441     1.39   0.164    -.0778812    .4567081
     verdade |   .1334492   .1534604     0.87   0.385    -.1682862    .4351845
         pr1 |  -.4514499    .152834    -2.95   0.003    -.7519536   -.1509463
         pr2 |  -.7670641   .1573006    -4.88   0.000     -1.07635   -.4577781
         pr3 |  -.4619451   .1478809    -3.12   0.002    -.7527101   -.1711801
        post |   .1082279   .1355204     0.80   0.425    -.1582336    .3746894
   post_miss |  -.1997235     .23187    -0.86   0.390    -.6556286    .2561816
      health |  -.2732482   .1145642    -2.39   0.018    -.4985054    -.047991
 health_miss |  -.0655562   .3167507    -0.21   0.836    -.6883545    .5572422
         sex |  -.1119143   .0948149    -1.18   0.239    -.2983402    .0745117
         age |   .0100979   .0039461     2.56   0.011     .0023389    .0178568
      single |   .2036038   .1172716     1.74   0.083    -.0269767    .4341844
       divor |    .012505   .5271068     0.02   0.981    -1.023898    1.048908
     protest |   .0388731   .1178769     0.33   0.742    -.1928975    .2706438
         com |   .0302524   .2332179     0.13   0.897    -.4283029    .4888077
        prof |   .4361661   .3282717     1.33   0.185     -.209285    1.081617
         tea |   .0554109   .1896832     0.29   0.770     -.317546    .4283678
     comform |  -.1836066   .4123465    -0.45   0.656    -.9943663    .6271532
         dom |   .0224803   .1380819     0.16   0.871    -.2490177    .2939784
    econfood |  -.0591245   .0422804    -1.40   0.163    -.1422567    .0240076
       house |   .0548452   .1325841     0.41   0.679     -.205843    .3155333
      llomue |   .2846217   .1818928     1.56   0.118    -.0730177    .6422611
     chitsua |  -.4118774   .4687002    -0.88   0.380     -1.33344    .5096856
      living |   -.012027   .0433842    -0.28   0.782    -.0973294    .0732755
       _cons |   .2168672   .2435328     0.89   0.374    -.2619694    .6957038
------------------------------------------------------------------------------

Simultaneous results for zscindepcne_3_2a, zscindepcne_3_3a

                                                  Number of obs   =       1020

                                             (Std. Err. adjusted for 161 clusters in ea)
----------------------------------------------------------------------------------------
                       |               Robust
                       |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
zscindepcne_3_2a_mean  |
             civiceduc |   .1019365   .0890097     1.15   0.252    -.0725193    .2763924
               hotline |    .167699   .0769915     2.18   0.029     .0167986    .3185995
               verdade |   .1189081     .08933     1.33   0.183    -.0561754    .2939917
                   pr1 |  -.3925323   .0916637    -4.28   0.000    -.5721898   -.2128747
                   pr2 |  -.8033415   .1047937    -7.67   0.000    -1.008733   -.5979496
                   pr3 |  -.4640241   .1005414    -4.62   0.000    -.6610817   -.2669665
                  post |   .0615132   .1137534     0.54   0.589    -.1614395    .2844658
             post_miss |  -.2697882   .1350202    -2.00   0.046     -.534423   -.0051535
                health |  -.1367187   .0732715    -1.87   0.062    -.2803281    .0068908
           health_miss |   .1298911   .2266729     0.57   0.567    -.3143796    .5741618
                   sex |   -.095533   .0599294    -1.59   0.111    -.2129925    .0219265
                   age |   .0033986   .0026558     1.28   0.201    -.0018067    .0086039
                single |   .0441265   .0796914     0.55   0.580    -.1120657    .2003187
                 divor |   .5263727   .1459162     3.61   0.000     .2403821    .8123633
               protest |   .0089152    .083177     0.11   0.915    -.1541088    .1719392
                   com |   .1735437   .1164467     1.49   0.136    -.0546876     .401775
                  prof |  -.1376085   .2826217    -0.49   0.626    -.6915369    .4163199
                   tea |   .0662102   .1436877     0.46   0.645    -.2154126    .3478331
               comform |  -.1084046   .2346924    -0.46   0.644    -.5683932    .3515841
                   dom |   .0028298   .0918574     0.03   0.975    -.1772074     .182867
              econfood |  -.0464355   .0275223    -1.69   0.092    -.1003783    .0075072
                 house |    .109221   .0922314     1.18   0.236    -.0715493    .2899913
                llomue |   .1463054    .120436     1.21   0.224    -.0897448    .3823556
               chitsua |  -.2374723   .2390528    -0.99   0.321    -.7060072    .2310626
                living |   .0432618    .034102     1.27   0.205     -.023577    .1101005
                 _cons |   .1890431   .1806148     1.05   0.295    -.1649555    .5430416
-----------------------+----------------------------------------------------------------
zscindepcne_3_2a_lnvar |
                 _cons |  -.2322115   .0609366    -3.81   0.000     -.351645    -.112778
-----------------------+----------------------------------------------------------------
zscindepcne_3_3a_mean  |
             civiceduc |   .1176178   .1396251     0.84   0.400    -.1560423     .391278
               hotline |   .1894135   .1225876     1.55   0.122    -.0508538    .4296807
               verdade |   .1334492   .1179218     1.13   0.258    -.0976734    .3645717
                   pr1 |  -.4514499   .1446437    -3.12   0.002    -.7349464   -.1679535
                   pr2 |  -.7670641    .152609    -5.03   0.000    -1.066172    -.467956
                   pr3 |  -.4619451   .1291843    -3.58   0.000    -.7151417   -.2087484
                  post |   .1082279   .1274288     0.85   0.396    -.1415279    .3579837
             post_miss |  -.1997235   .1162827    -1.72   0.086    -.4276334    .0281863
                health |  -.2732482   .1159615    -2.36   0.018    -.5005286   -.0459678
           health_miss |  -.0655562   .2501817    -0.26   0.793    -.5559032    .4247909
                   sex |  -.1119143   .0920299    -1.22   0.224    -.2922895     .068461
                   age |   .0100979   .0040495     2.49   0.013      .002161    .0180347
                single |   .2036038    .091069     2.24   0.025     .0251119    .3820958
                 divor |    .012505   .3056948     0.04   0.967    -.5866457    .6116557
               protest |   .0388731   .1192382     0.33   0.744    -.1948295    .2725757
                   com |   .0302524   .2108746     0.14   0.886    -.3830543    .4435591
                  prof |   .4361661   .1084532     4.02   0.000     .2236017    .6487305
                   tea |   .0554109   .2222927     0.25   0.803    -.3802748    .4910965
               comform |  -.1836066   .2676544    -0.69   0.493    -.7081996    .3409865
                   dom |   .0224803   .1360407     0.17   0.869    -.2441544    .2891151
              econfood |  -.0591245   .0420364    -1.41   0.160    -.1415143    .0232652
                 house |   .0548452    .124484     0.44   0.660    -.1891391    .2988294
                llomue |   .2846217   .1979578     1.44   0.150    -.1033684    .6726118
               chitsua |  -.4118774   .2087475    -1.97   0.048     -.821015   -.0027398
                living |   -.012027   .0460697    -0.26   0.794    -.1023219     .078268
                 _cons |   .2168672    .256716     0.84   0.398    -.2862869    .7200213
-----------------------+----------------------------------------------------------------
zscindepcne_3_3a_lnvar |
                 _cons |  -.2226376   .0846163    -2.63   0.009    -.3884824   -.0567927
----------------------------------------------------------------------------------------

 ( 1)  [zscindepcne_3_2a_mean]civiceduc - [zscindepcne_3_3a_mean]civiceduc = 0

           chi2(  1) =    0.01
         Prob > chi2 =    0.9095
.90945108

 ( 1)  [zscindepcne_3_2a_mean]hotline - [zscindepcne_3_3a_mean]hotline = 0

           chi2(  1) =    0.04
         Prob > chi2 =    0.8443
.84432841

 ( 1)  [zscindepcne_3_2a_mean]verdade - [zscindepcne_3_3a_mean]verdade = 0

           chi2(  1) =    0.02
         Prob > chi2 =    0.8978
.89780414

. 
. matrix define means=(m_zscindepcne_2_1, m_zscindepcne_3_1 \ t_zscindepcne_2_1_1, t_zscindepcne
> _3_1_1 \ t_zscindepcne_2_1_2, t_zscindepcne_3_1_2 \ t_zscindepcne_2_1_3, t_zscindepcne_3_1_3 \
>  t_zscindepcne_2_1_4, t_zscindepcne_3_1_4 \ t_zscindepcne_2_5, t_zscindepcne_3_5 \ t_zscindepc
> ne_2_6, t_zscindepcne_3_6 \ t_zscindepcne_2_7, t_zscindepcne_3_7)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_survey.xml") append sheet("indepc
> ne") 


note: results saved to outputregs_survey.xml

. xml_tab $list2, save("outputregs_survey.xml") append sheet("indepcne stats") 


note: results saved to outputregs_survey.xml

. estimates clear

. 
. *confusion
. 
. global final="zzscconfusion"

. 
. global list1=""

. global list2=""

. 
. foreach i in $final {
  2. 
.         regress `i' $treat $prov if time==1, cluster(ea)
  3.         estimates store `i'_2_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2_1=r(mean)
  6.         display m_`i'_2_1
  7.         test civiceduc = hotline
  8.         scalar define t_`i'_2_1_1=r(p)
  9.         display t_`i'_2_1_1
 10.         test civiceduc = verdade
 11.         scalar define t_`i'_2_1_2=r(p)
 12.         display t_`i'_2_1_2
 13.         test hotline = verdade
 14.         scalar define t_`i'_2_1_3=r(p)
 15.         display t_`i'_2_1_3
 16.         test civiceduc hotline verdade
 17.         scalar define t_`i'_2_1_4=r(p)
 18.         display t_`i'_2_1_4
 19. 
.         regress `i' $treat $prov if time==1 & lazy==0, cluster(ea)
 20.         estimates store `i'_2_2
 21.         regress `i' $treat $prov if time==1 & lazy==0
 22.         estimates store `i'_2_2a
 23.         
.         regress `i' $treat $prov if time==1 & (lazy==1|control==1), cluster(ea)
 24.         estimates store `i'_2_3
 25.         regress `i' $treat $prov if time==1 & (lazy==1|control==1)
 26.         estimates store `i'_2_3a
 27. 
.         suest `i'_2_2a `i'_2_3a, cluster(ea)
 28.         test [`i'_2_2a_mean]civiceduc=[`i'_2_3a_mean]civiceduc  
 29.         scalar define t_`i'_2_5=r(p)
 30.         display t_`i'_2_5
 31.         test [`i'_2_2a_mean]hotline=[`i'_2_3a_mean]hotline      
 32.         scalar define t_`i'_2_6=r(p)
 33.         display t_`i'_2_6
 34.         test [`i'_2_2a_mean]verdade=[`i'_2_3a_mean]verdade
 35.         scalar define t_`i'_2_7=r(p)
 36.         display t_`i'_2_7
 37. 
.         regress `i' $treat $prov $ea $controls if time==1, cluster(ea)
 38.         estimates store `i'_3_1
 39.         sum `i' if e(sample) & control == 1
 40.         scalar define m_`i'_3_1=r(mean)
 41.         display m_`i'_3_1
 42.         test civiceduc = hotline
 43.         scalar define t_`i'_3_1_1=r(p)
 44.         display t_`i'_3_1_1
 45.         test civiceduc = verdade
 46.         scalar define t_`i'_3_1_2=r(p)
 47.         display t_`i'_3_1_2
 48.         test hotline = verdade
 49.         scalar define t_`i'_3_1_3=r(p)
 50.         display t_`i'_3_1_3
 51.         test civiceduc hotline verdade
 52.         scalar define t_`i'_3_1_4=r(p)
 53.         display t_`i'_3_1_4
 54. 
.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0, cluster(ea)
 55.         estimates store `i'_3_2
 56.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0
 57.         estimates store `i'_3_2a
 58.         
.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1), cluster(ea)
 59.         estimates store `i'_3_3
 60.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1)
 61.         estimates store `i'_3_3a
 62. 
.         suest `i'_3_2a `i'_3_3a, cluster(ea)
 63.         test [`i'_3_2a_mean]civiceduc=[`i'_3_3a_mean]civiceduc  
 64.         scalar define t_`i'_3_5=r(p)
 65.         display t_`i'_3_5
 66.         test [`i'_3_2a_mean]hotline=[`i'_3_3a_mean]hotline      
 67.         scalar define t_`i'_3_6=r(p)
 68.         display t_`i'_3_6
 69.         test [`i'_3_2a_mean]verdade=[`i'_3_3a_mean]verdade
 70.         scalar define t_`i'_3_7=r(p)
 71.         display t_`i'_3_7
 72.         
.         global list1="$list1" + " `i'_2_1" + " `i'_3_1" + " `i'_2_2" + " `i'_3_2"  + " `i'_2_3
> " + " `i'_3_3"
 73.         
.         }

Linear regression                                      Number of obs =     814
                                                       F(  6,   155) =    5.41
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0446
                                                       Root MSE      =  .59203

                                   (Std. Err. adjusted for 156 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zzscconfus~n |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.028098   .0665587    -0.42   0.673    -.1595772    .1033812
     hotline |  -.1970828   .0601875    -3.27   0.001    -.3159765   -.0781891
     verdade |   -.157592   .0662347    -2.38   0.019    -.2884311   -.0267528
         pr1 |   .2414363   .0577123     4.18   0.000     .1274322    .3554405
         pr2 |   .1583489   .0556361     2.85   0.005     .0484461    .2682517
         pr3 |   .0865717   .0547142     1.58   0.116      -.02151    .1946535
       _cons |  -.1170103   .0536927    -2.18   0.031    -.2230742   -.0109464
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zzscconfus~n |       194    6.14e-10    .6349961  -.8425885   1.182998
6.145e-10

 ( 1)  civiceduc - hotline = 0

       F(  1,   155) =    9.04
            Prob > F =    0.0031
.0030856

 ( 1)  civiceduc - verdade = 0

       F(  1,   155) =    4.24
            Prob > F =    0.0411
.04105332

 ( 1)  hotline - verdade = 0

       F(  1,   155) =    0.48
            Prob > F =    0.4883
.4882562

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   155) =    5.34
            Prob > F =    0.0016
.00157713

Linear regression                                      Number of obs =     696
                                                       F(  6,   153) =    5.14
                                                       Prob > F      =  0.0001
                                                       R-squared     =  0.0503
                                                       Root MSE      =  .59334

                                   (Std. Err. adjusted for 154 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zzscconfus~n |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0109178     .06837    -0.16   0.873    -.1459888    .1241532
     hotline |  -.1952817   .0633989    -3.08   0.002     -.320532   -.0700315
     verdade |  -.1339837   .0675455    -1.98   0.049     -.267426   -.0005415
         pr1 |   .2726556   .0617735     4.41   0.000     .1506164    .3946948
         pr2 |   .1521647   .0623149     2.44   0.016      .029056    .2752733
         pr3 |   .0749697   .0605814     1.24   0.218    -.0447143    .1946537
       _cons |  -.1210053    .055109    -2.20   0.030     -.229878   -.0121325
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     696
-------------+------------------------------           F(  6,   689) =    6.08
       Model |  12.8506986     6   2.1417831           Prob > F      =  0.0000
    Residual |  242.561459   689  .352048562           R-squared     =  0.0503
-------------+------------------------------           Adj R-squared =  0.0420
       Total |  255.412158   695  .367499508           Root MSE      =  .59334

------------------------------------------------------------------------------
zzscconfus~n |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0109178   .0621816    -0.18   0.861    -.1330059    .1111704
     hotline |  -.1952817   .0622168    -3.14   0.002    -.3174391   -.0731244
     verdade |  -.1339837   .0636933    -2.10   0.036    -.2590399   -.0089275
         pr1 |   .2726556   .0600987     4.54   0.000     .1546571    .3906541
         pr2 |   .1521647   .0680203     2.24   0.026     .0186127    .2857167
         pr3 |   .0749697   .0614966     1.22   0.223    -.0457736     .195713
       _cons |  -.1210053   .0560839    -2.16   0.031    -.2311212   -.0108894
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     312
                                                       F(  6,   118) =    3.53
                                                       Prob > F      =  0.0030
                                                       R-squared     =  0.0519
                                                       Root MSE      =   .6106

                                   (Std. Err. adjusted for 119 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zzscconfus~n |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1087875   .1084032    -1.00   0.318    -.3234553    .1058803
     hotline |  -.2044511    .091251    -2.24   0.027     -.385153   -.0237493
     verdade |  -.2615703   .1087369    -2.41   0.018    -.4768989   -.0462417
         pr1 |   .2288264   .1016518     2.25   0.026     .0275281    .4301247
         pr2 |   .2211238   .0795966     2.78   0.006     .0635009    .3787467
         pr3 |   .1433269   .0901893     1.59   0.115    -.0352725    .3219264
       _cons |  -.1407225    .061214    -2.30   0.023     -.261943    -.019502
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     312
-------------+------------------------------           F(  6,   305) =    2.78
       Model |  6.22167617     6  1.03694603           Prob > F      =  0.0120
    Residual |  113.714572   305  .372834663           R-squared     =  0.0519
-------------+------------------------------           Adj R-squared =  0.0332
       Total |  119.936248   311  .385647101           Root MSE      =   .6106

------------------------------------------------------------------------------
zzscconfus~n |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1087875   .1097691    -0.99   0.322     -.324788    .1072131
     hotline |  -.2044511   .1015531    -2.01   0.045    -.4042846   -.0046177
     verdade |  -.2615703   .1110214    -2.36   0.019    -.4800352   -.0431053
         pr1 |   .2288264   .0925163     2.47   0.014     .0467754    .4108774
         pr2 |   .2211238   .1103654     2.00   0.046     .0039498    .4382977
         pr3 |   .1433269   .0911536     1.57   0.117    -.0360425    .3226964
       _cons |  -.1407225   .0708046    -1.99   0.048    -.2800497   -.0013952
------------------------------------------------------------------------------

Simultaneous results for zzscconfusion_2_2a, zzscconfusion_2_3a

                                                  Number of obs   =        814

                                               (Std. Err. adjusted for 156 clusters in ea)
------------------------------------------------------------------------------------------
                         |               Robust
                         |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
zzscconfusion_2_2a_mean  |
               civiceduc |  -.0109178   .0680713    -0.16   0.873    -.1443352    .1224996
                 hotline |  -.1952817    .063122    -3.09   0.002    -.3189986   -.0715649
                 verdade |  -.1339837   .0672505    -1.99   0.046    -.2657923   -.0021752
                     pr1 |   .2726556   .0615037     4.43   0.000     .1521105    .3932007
                     pr2 |   .1521647   .0620427     2.45   0.014     .0305632    .2737661
                     pr3 |   .0749697   .0603168     1.24   0.214     -.043249    .1931885
                   _cons |  -.1210053   .0548683    -2.21   0.027    -.2285451   -.0134654
-------------------------+----------------------------------------------------------------
zzscconfusion_2_2a_lnvar |
                   _cons |  -1.043986   .0493199   -21.17   0.000    -1.140651    -.947321
-------------------------+----------------------------------------------------------------
zzscconfusion_2_3a_mean  |
               civiceduc |  -.1087875   .1072447    -1.01   0.310    -.3189831    .1014082
                 hotline |  -.2044511   .0902758    -2.26   0.024    -.3813885   -.0275138
                 verdade |  -.2615703   .1075748    -2.43   0.015     -.472413   -.0507276
                     pr1 |   .2288264   .1005655     2.28   0.023     .0317217    .4259311
                     pr2 |   .2211238   .0787459     2.81   0.005     .0667846    .3754629
                     pr3 |   .1433269   .0892255     1.61   0.108    -.0315518    .3182057
                   _cons |  -.1407225   .0605598    -2.32   0.020    -.2594176   -.0220274
-------------------------+----------------------------------------------------------------
zzscconfusion_2_3a_lnvar |
                   _cons |  -.9866202   .0652572   -15.12   0.000    -1.114522   -.8587184
------------------------------------------------------------------------------------------

 ( 1)  [zzscconfusion_2_2a_mean]civiceduc - [zzscconfusion_2_3a_mean]civiceduc = 0

           chi2(  1) =    0.99
         Prob > chi2 =    0.3199
.31992119

 ( 1)  [zzscconfusion_2_2a_mean]hotline - [zzscconfusion_2_3a_mean]hotline = 0

           chi2(  1) =    0.01
         Prob > chi2 =    0.9165
.91651278

 ( 1)  [zzscconfusion_2_2a_mean]verdade - [zzscconfusion_2_3a_mean]verdade = 0

           chi2(  1) =    1.68
         Prob > chi2 =    0.1954
.19544649

Linear regression                                      Number of obs =     804
                                                       F( 25,   155) =    6.00
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1164
                                                       Root MSE      =  .57556

                                   (Std. Err. adjusted for 156 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zzscconfus~n |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0872376   .0644463    -1.35   0.178    -.2145441    .0400689
     hotline |  -.2216271   .0575899    -3.85   0.000    -.3353895   -.1078647
     verdade |  -.2129143   .0585259    -3.64   0.000    -.3285255    -.097303
         pr1 |   .2996587   .0637709     4.70   0.000     .1736866    .4256309
         pr2 |   .1148846   .0705175     1.63   0.105    -.0244148     .254184
         pr3 |   .0479028   .0678826     0.71   0.481    -.0861916    .1819972
        post |  -.0348763   .0647409    -0.54   0.591    -.1627646     .093012
   post_miss |  -.1137302   .1173794    -0.97   0.334    -.3456001    .1181396
      health |  -.1023757   .0472872    -2.16   0.032    -.1957862   -.0089651
 health_miss |  -.0090735   .1328232    -0.07   0.946    -.2714507    .2533036
         sex |  -.1072273   .0452762    -2.37   0.019    -.1966653   -.0177894
         age |   .0025589   .0019445     1.32   0.190    -.0012823       .0064
      single |  -.0906709   .0496979    -1.82   0.070    -.1888434    .0075016
       divor |   .3719073   .2939838     1.27   0.208    -.2088244    .9526391
     protest |   .0252284   .0646987     0.39   0.697    -.1025766    .1530333
         com |   .1277823   .0963018     1.33   0.186    -.0624511    .3180156
        prof |    -.26591   .1393218    -1.91   0.058    -.5411245    .0093045
         tea |   -.385916   .0776072    -4.97   0.000    -.5392203   -.2326117
     comform |  -.0800381   .1491535    -0.54   0.592     -.374674    .2145978
         dom |   .0239077   .0666931     0.36   0.720     -.107837    .1556524
    econfood |   .0296977   .0169342     1.75   0.081    -.0037538    .0631493
       house |   .0002291   .0525692     0.00   0.997    -.1036155    .1040736
      llomue |  -.1357298   .0862271    -1.57   0.118    -.3060616    .0346021
     chitsua |   .3078975   .2098088     1.47   0.144    -.1065561    .7223512
      living |   .0150891   .0208763     0.72   0.471    -.0261497    .0563279
       _cons |   -.089365   .1097802    -0.81   0.417    -.3062233    .1274933
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zzscconfus~n |       192    .0052182    .6361474  -.8425885   1.182998
.00521824

 ( 1)  civiceduc - hotline = 0

       F(  1,   155) =    5.16
            Prob > F =    0.0245
.0244555

 ( 1)  civiceduc - verdade = 0

       F(  1,   155) =    4.61
            Prob > F =    0.0334
.03336744

 ( 1)  hotline - verdade = 0

       F(  1,   155) =    0.03
            Prob > F =    0.8720
.87202511

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   155) =    6.79
            Prob > F =    0.0002
.00024875

Linear regression                                      Number of obs =     690
                                                       F( 25,   153) =    5.92
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1127
                                                       Root MSE      =  .58038

                                   (Std. Err. adjusted for 154 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zzscconfus~n |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0913087   .0664263    -1.37   0.171    -.2225398    .0399223
     hotline |   -.237559   .0590754    -4.02   0.000    -.3542679   -.1208502
     verdade |  -.1931767   .0613072    -3.15   0.002    -.3142945   -.0720589
         pr1 |   .3412938   .0673425     5.07   0.000     .2082525     .474335
         pr2 |    .116672   .0781576     1.49   0.138    -.0377354    .2710795
         pr3 |   .0482339   .0736952     0.65   0.514    -.0973577    .1938254
        post |  -.0781999   .0616372    -1.27   0.206    -.1999698      .04357
   post_miss |  -.0986374   .1184666    -0.83   0.406    -.3326789     .135404
      health |  -.1110109   .0541683    -2.05   0.042    -.2180253   -.0039964
 health_miss |  -.0441876   .1308696    -0.34   0.736    -.3027323     .214357
         sex |  -.0920392   .0484764    -1.90   0.059    -.1878087    .0037303
         age |   .0028706   .0021894     1.31   0.192    -.0014547    .0071959
      single |  -.0564535   .0560593    -1.01   0.316    -.1672036    .0542967
       divor |   .0661548   .3657307     0.18   0.857    -.6563792    .7886888
     protest |   .0316068   .0709898     0.45   0.657    -.1086399    .1718536
         com |   .1813426   .1021078     1.78   0.078    -.0203805    .3830657
        prof |  -.2747231   .1652154    -1.66   0.098    -.6011209    .0516748
         tea |  -.2876954   .0888605    -3.24   0.001    -.4632473   -.1121435
     comform |    .003873    .169757     0.02   0.982    -.3314974    .3392433
         dom |   .0360269   .0734398     0.49   0.624      -.10906    .1811137
    econfood |   .0241931   .0177928     1.36   0.176    -.0109581    .0593444
       house |   .0248003   .0589481     0.42   0.675    -.0916571    .1412576
      llomue |  -.2194469   .0889107    -2.47   0.015    -.3950979   -.0437958
     chitsua |   .3915315   .2266639     1.73   0.086    -.0562636    .8393265
      living |   .0104769   .0219109     0.48   0.633      -.03281    .0537639
       _cons |  -.1128992   .1240066    -0.91   0.364    -.3578854     .132087
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     690
-------------+------------------------------           F( 25,   664) =    3.37
       Model |  28.4088979    25  1.13635592           Prob > F      =  0.0000
    Residual |  223.662091   664  .336840499           R-squared     =  0.1127
-------------+------------------------------           Adj R-squared =  0.0793
       Total |  252.070989   689  .365850493           Root MSE      =  .58038

------------------------------------------------------------------------------
zzscconfus~n |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0913087   .0641111    -1.42   0.155    -.2171936    .0345762
     hotline |   -.237559   .0628513    -3.78   0.000    -.3609703   -.1141478
     verdade |  -.1931767   .0652236    -2.96   0.003    -.3212461   -.0651073
         pr1 |   .3412938   .0727469     4.69   0.000     .1984522    .4841354
         pr2 |    .116672   .0782418     1.49   0.136    -.0369592    .2703033
         pr3 |   .0482339   .0709392     0.68   0.497    -.0910582     .187526
        post |  -.0781999   .0778811    -1.00   0.316    -.2311228    .0747229
   post_miss |  -.0986374   .1232319    -0.80   0.424    -.3406085    .1433336
      health |  -.1110109   .0543616    -2.04   0.042    -.2177521   -.0042696
 health_miss |  -.0441876   .1544539    -0.29   0.775    -.3474645    .2590893
         sex |  -.0920392    .047301    -1.95   0.052    -.1849167    .0008383
         age |   .0028706   .0018573     1.55   0.123    -.0007763    .0065175
      single |  -.0564535   .0611616    -0.92   0.356    -.1765469    .0636399
       divor |   .0661548   .2974392     0.22   0.824    -.5178798    .6501894
     protest |   .0316068   .0563223     0.56   0.575    -.0789844    .1421981
         com |   .1813426   .0997787     1.82   0.070    -.0145771    .3772623
        prof |  -.2747231   .1658478    -1.66   0.098    -.6003723    .0509262
         tea |  -.2876954   .1080862    -2.66   0.008    -.4999274   -.0754634
     comform |    .003873   .1888942     0.02   0.984     -.367029     .374775
         dom |   .0360269   .0711771     0.51   0.613    -.1037324    .1757861
    econfood |   .0241931   .0196146     1.23   0.218    -.0143209    .0627072
       house |   .0248003   .0683279     0.36   0.717    -.1093645     .158965
      llomue |  -.2194469     .08634    -2.54   0.011    -.3889792   -.0499146
     chitsua |   .3915315   .2149273     1.82   0.069    -.0304875    .8135504
      living |   .0104769   .0225449     0.46   0.642    -.0337909    .0547448
       _cons |  -.1128992    .124818    -0.90   0.366    -.3579848    .1321864
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     306
                                                       F( 25,   118) =    6.22
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1542
                                                       Root MSE      =  .59832

                                   (Std. Err. adjusted for 119 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zzscconfus~n |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0849357   .0987479    -0.86   0.391    -.2804835    .1106121
     hotline |  -.2125624   .1020574    -2.08   0.039    -.4146638    -.010461
     verdade |  -.3319221   .1093145    -3.04   0.003    -.5483945   -.1154497
         pr1 |   .2589049   .0920182     2.81   0.006     .0766838     .441126
         pr2 |   .1700543    .119306     1.43   0.157     -.066204    .4063126
         pr3 |   .0897096   .1198717     0.75   0.456    -.1476691    .3270883
        post |  -.0131413   .0952304    -0.14   0.890    -.2017235    .1754408
   post_miss |  -.0802484   .1453446    -0.55   0.582    -.3680703    .2075736
      health |  -.1918178   .0858976    -2.23   0.027    -.3619184   -.0217171
 health_miss |  -.1189091   .2485953    -0.48   0.633    -.6111954    .3733772
         sex |   -.107384    .071687    -1.50   0.137    -.2493437    .0345758
         age |   .0016334   .0033048     0.49   0.622     -.004911    .0081778
      single |  -.1595044   .0847265    -1.88   0.062    -.3272859     .008277
       divor |   .3404296   .5790615     0.59   0.558    -.8062697    1.487129
     protest |   .0409368   .1215967     0.34   0.737    -.1998578    .2817314
         com |   .0302496   .1930301     0.16   0.876    -.3520025    .4125017
        prof |  -.2385102   .2464042    -0.97   0.335    -.7264575    .2494371
         tea |  -.5262415   .1037081    -5.07   0.000    -.7316119   -.3208711
     comform |   .0380118   .3110755     0.12   0.903    -.5780024     .654026
         dom |  -.0545477     .12676    -0.43   0.668     -.305567    .1964716
    econfood |   .0379634   .0321443     1.18   0.240    -.0256911    .1016179
       house |    .007974   .0984664     0.08   0.936    -.1870162    .2029642
      llomue |   -.094497   .1679346    -0.56   0.575    -.4270533    .2380592
     chitsua |   .2811727    .342978     0.82   0.414    -.3980172    .9603625
      living |  -.0006935   .0321928    -0.02   0.983    -.0644441     .063057
       _cons |   .0613933    .177172     0.35   0.730    -.2894555    .4122421
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     306
-------------+------------------------------           F( 25,   280) =    2.04
       Model |  18.2769788    25  .731079153           Prob > F      =  0.0030
    Residual |  100.235759   280  .357984855           R-squared     =  0.1542
-------------+------------------------------           Adj R-squared =  0.0787
       Total |  118.512738   305  .388566355           Root MSE      =  .59832

------------------------------------------------------------------------------
zzscconfus~n |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0849357   .1141404    -0.74   0.457     -.309618    .1397465
     hotline |  -.2125624   .1071807    -1.98   0.048    -.4235447   -.0015801
     verdade |  -.3319221   .1179654    -2.81   0.005    -.5641337   -.0997105
         pr1 |   .2589049   .1170863     2.21   0.028     .0284238    .4893861
         pr2 |   .1700543   .1276557     1.33   0.184    -.0812325     .421341
         pr3 |   .0897096   .1129397     0.79   0.428    -.1326091    .3120283
        post |  -.0131413   .1042848    -0.13   0.900    -.2184232    .1921405
   post_miss |  -.0802484   .1663418    -0.48   0.630    -.4076876    .2471909
      health |  -.1918178   .0918917    -2.09   0.038    -.3727041   -.0109314
 health_miss |  -.1189091   .3759349    -0.32   0.752    -.8589266    .6211083
         sex |   -.107384   .0732835    -1.47   0.144    -.2516405    .0368726
         age |   .0016334   .0031181     0.52   0.601    -.0045045    .0077713
      single |  -.1595044   .0904416    -1.76   0.079    -.3375363    .0185275
       divor |   .3404296   .3556486     0.96   0.339    -.3596549    1.040514
     protest |   .0409368    .092585     0.44   0.659    -.1413143    .2231879
         com |   .0302496   .1742691     0.17   0.862    -.3127944    .3732936
        prof |  -.2385102   .2388284    -1.00   0.319    -.7086373    .2316169
         tea |  -.5262415   .1437018    -3.66   0.000    -.8091146   -.2433684
     comform |   .0380118   .2804333     0.14   0.892    -.5140135    .5900371
         dom |  -.0545477   .1157249    -0.47   0.638     -.282349    .1732536
    econfood |   .0379634   .0318452     1.19   0.234    -.0247231    .1006499
       house |    .007974   .1114019     0.07   0.943    -.2113175    .2272655
      llomue |   -.094497   .1371009    -0.69   0.491    -.3643763    .1753822
     chitsua |   .2811727   .4430538     0.63   0.526    -.5909665    1.153312
      living |  -.0006935   .0348332    -0.02   0.984    -.0692617    .0678747
       _cons |   .0613933    .196096     0.31   0.754    -.3246163    .4474029
------------------------------------------------------------------------------

Simultaneous results for zzscconfusion_3_2a, zzscconfusion_3_3a

                                                  Number of obs   =        804

                                               (Std. Err. adjusted for 156 clusters in ea)
------------------------------------------------------------------------------------------
                         |               Robust
                         |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
zzscconfusion_3_2a_mean  |
               civiceduc |  -.0913087   .0652073    -1.40   0.161    -.2191126    .0364952
                 hotline |   -.237559   .0579914    -4.10   0.000      -.35122   -.1238981
                 verdade |  -.1931767   .0601821    -3.21   0.001    -.3111315    -.075222
                     pr1 |   .3412938   .0661067     5.16   0.000      .211727    .4708606
                     pr2 |    .116672   .0767234     1.52   0.128     -.033703    .2670471
                     pr3 |   .0482339   .0723428     0.67   0.505    -.0935555    .1900232
                    post |  -.0781999   .0605061    -1.29   0.196    -.1967898    .0403899
               post_miss |  -.0986374   .1162926    -0.85   0.396    -.3265668    .1292919
                  health |  -.1110109   .0531743    -2.09   0.037    -.2152305   -.0067912
             health_miss |  -.0441876    .128468    -0.34   0.731    -.2959803     .207605
                     sex |  -.0920392   .0475868    -1.93   0.053    -.1853077    .0012292
                     age |   .0028706   .0021492     1.34   0.182    -.0013418    .0070829
                  single |  -.0564535   .0550305    -1.03   0.305    -.1643113    .0514044
                   divor |   .0661548   .3590192     0.18   0.854    -.6375098    .7698194
                 protest |   .0316068   .0696871     0.45   0.650    -.1049773     .168191
                     com |   .1813426    .100234     1.81   0.070    -.0151124    .3777976
                    prof |  -.2747231   .1621835    -1.69   0.090    -.5925969    .0431507
                     tea |  -.2876954   .0872298    -3.30   0.001    -.4586626   -.1167281
                 comform |    .003873   .1666418     0.02   0.981     -.322739     .330485
                     dom |   .0360269   .0720921     0.50   0.617     -.105271    .1773247
                econfood |   .0241931   .0174663     1.39   0.166    -.0100401    .0584264
                   house |   .0248003   .0578664     0.43   0.668    -.0886157    .1382163
                  llomue |  -.2194469   .0872791    -2.51   0.012    -.3905107    -.048383
                 chitsua |   .3915315   .2225044     1.76   0.078    -.0445692    .8276321
                  living |   .0104769   .0215088     0.49   0.626    -.0316795    .0526334
                   _cons |  -.1128992   .1217309    -0.93   0.354    -.3514874    .1256891
-------------------------+----------------------------------------------------------------
zzscconfusion_3_2a_lnvar |
                   _cons |  -1.088146   .0517944   -21.01   0.000    -1.189661   -.9866307
-------------------------+----------------------------------------------------------------
zzscconfusion_3_3a_mean  |
               civiceduc |  -.0849357   .0945194    -0.90   0.369    -.2701904    .1003189
                 hotline |  -.2125624   .0976872    -2.18   0.030    -.4040257    -.021099
                 verdade |  -.3319221   .1046335    -3.17   0.002        -.537   -.1268442
                     pr1 |   .2589049   .0880779     2.94   0.003     .0862755    .4315344
                     pr2 |   .1700543   .1141971     1.49   0.136     -.053768    .3938766
                     pr3 |   .0897096   .1147387     0.78   0.434    -.1351741    .3145933
                    post |  -.0131413   .0911525    -0.14   0.885     -.191797    .1655144
               post_miss |  -.0802484   .1391208    -0.58   0.564    -.3529201    .1924234
                  health |  -.1918178   .0822194    -2.33   0.020    -.3529648   -.0306708
             health_miss |  -.1189091   .2379501    -0.50   0.617    -.5852828    .3474645
                     sex |   -.107384   .0686173    -1.56   0.118    -.2418713    .0271034
                     age |   .0016334   .0031633     0.52   0.606    -.0045665    .0078333
                  single |  -.1595044   .0810984    -1.97   0.049    -.3184543   -.0005545
                   divor |   .3404296   .5542654     0.61   0.539    -.7459105     1.42677
                 protest |   .0409368   .1163898     0.35   0.725     -.187183    .2690566
                     com |   .0302496   .1847643     0.16   0.870    -.3318817    .3923809
                    prof |  -.2385102   .2358528    -1.01   0.312    -.7007733    .2237529
                     tea |  -.5262415   .0992672    -5.30   0.000    -.7208017   -.3316813
                 comform |   .0380118   .2977549     0.13   0.898    -.5455771    .6216007
                     dom |  -.0545477    .121332    -0.45   0.653     -.292354    .1832586
                econfood |   .0379634   .0307678     1.23   0.217    -.0223405    .0982673
                   house |    .007974   .0942499     0.08   0.933    -.1767525    .1927004
                  llomue |   -.094497   .1607434    -0.59   0.557    -.4095484    .2205543
                 chitsua |   .2811727   .3282913     0.86   0.392    -.3622664    .9246117
                  living |  -.0006935   .0308143    -0.02   0.982    -.0610884    .0597014
                   _cons |   .0613933   .1695853     0.36   0.717    -.2709878    .3937743
-------------------------+----------------------------------------------------------------
zzscconfusion_3_3a_lnvar |
                   _cons |  -1.027265   .0667675   -15.39   0.000    -1.158126   -.8964027
------------------------------------------------------------------------------------------

 ( 1)  [zzscconfusion_3_2a_mean]civiceduc - [zzscconfusion_3_3a_mean]civiceduc = 0

           chi2(  1) =    0.01
         Prob > chi2 =    0.9425
.9425309

 ( 1)  [zzscconfusion_3_2a_mean]hotline - [zzscconfusion_3_3a_mean]hotline = 0

           chi2(  1) =    0.07
         Prob > chi2 =    0.7942
.79419169

 ( 1)  [zzscconfusion_3_2a_mean]verdade - [zzscconfusion_3_3a_mean]verdade = 0

           chi2(  1) =    1.53
         Prob > chi2 =    0.2158
.21581163

. 
. matrix define means=(m_zzscconfusion_2_1, m_zzscconfusion_3_1 \ t_zzscconfusion_2_1_1, t_zzscc
> onfusion_3_1_1 \ t_zzscconfusion_2_1_2, t_zzscconfusion_3_1_2 \ t_zzscconfusion_2_1_3, t_zzscc
> onfusion_3_1_3 \ t_zzscconfusion_2_1_4, t_zzscconfusion_3_1_4 \ t_zzscconfusion_2_5, t_zzsccon
> fusion_3_5 \ t_zzscconfusion_2_6, t_zzscconfusion_3_6 \ t_zzscconfusion_2_7, t_zzscconfusion_3
> _7)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_survey.xml") append sheet("confus
> ion") 


note: results saved to outputregs_survey.xml

. xml_tab $list2, save("outputregs_survey.xml") append sheet("confusion stats") 


note: results saved to outputregs_survey.xml

. estimates clear

. 
. *freefair2009
. 
. global final="zscfreefair2009"

. 
. global list1=""

. global list2=""

. 
. foreach i in $final {
  2. 
.         regress `i' $treat $prov if time==1, cluster(ea)
  3.         estimates store `i'_2_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2_1=r(mean)
  6.         display m_`i'_2_1
  7.         test civiceduc = hotline
  8.         scalar define t_`i'_2_1_1=r(p)
  9.         display t_`i'_2_1_1
 10.         test civiceduc = verdade
 11.         scalar define t_`i'_2_1_2=r(p)
 12.         display t_`i'_2_1_2
 13.         test hotline = verdade
 14.         scalar define t_`i'_2_1_3=r(p)
 15.         display t_`i'_2_1_3
 16.         test civiceduc hotline verdade
 17.         scalar define t_`i'_2_1_4=r(p)
 18.         display t_`i'_2_1_4
 19. 
.         regress `i' $treat $prov if time==1 & lazy==0, cluster(ea)
 20.         estimates store `i'_2_2
 21.         regress `i' $treat $prov if time==1 & lazy==0
 22.         estimates store `i'_2_2a
 23.         
.         regress `i' $treat $prov if time==1 & (lazy==1|control==1), cluster(ea)
 24.         estimates store `i'_2_3
 25.         regress `i' $treat $prov if time==1 & (lazy==1|control==1)
 26.         estimates store `i'_2_3a
 27. 
.         suest `i'_2_2a `i'_2_3a, cluster(ea)
 28.         test [`i'_2_2a_mean]civiceduc=[`i'_2_3a_mean]civiceduc  
 29.         scalar define t_`i'_2_5=r(p)
 30.         display t_`i'_2_5
 31.         test [`i'_2_2a_mean]hotline=[`i'_2_3a_mean]hotline      
 32.         scalar define t_`i'_2_6=r(p)
 33.         display t_`i'_2_6
 34.         test [`i'_2_2a_mean]verdade=[`i'_2_3a_mean]verdade
 35.         scalar define t_`i'_2_7=r(p)
 36.         display t_`i'_2_7
 37. 
.         regress `i' $treat $prov $ea $controls if time==1, cluster(ea)
 38.         estimates store `i'_3_1
 39.         sum `i' if e(sample) & control == 1
 40.         scalar define m_`i'_3_1=r(mean)
 41.         display m_`i'_3_1
 42.         test civiceduc = hotline
 43.         scalar define t_`i'_3_1_1=r(p)
 44.         display t_`i'_3_1_1
 45.         test civiceduc = verdade
 46.         scalar define t_`i'_3_1_2=r(p)
 47.         display t_`i'_3_1_2
 48.         test hotline = verdade
 49.         scalar define t_`i'_3_1_3=r(p)
 50.         display t_`i'_3_1_3
 51.         test civiceduc hotline verdade
 52.         scalar define t_`i'_3_1_4=r(p)
 53.         display t_`i'_3_1_4
 54. 
.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0, cluster(ea)
 55.         estimates store `i'_3_2
 56.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0
 57.         estimates store `i'_3_2a
 58.         
.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1), cluster(ea)
 59.         estimates store `i'_3_3
 60.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1)
 61.         estimates store `i'_3_3a
 62. 
.         suest `i'_3_2a `i'_3_3a, cluster(ea)
 63.         test [`i'_3_2a_mean]civiceduc=[`i'_3_3a_mean]civiceduc  
 64.         scalar define t_`i'_3_5=r(p)
 65.         display t_`i'_3_5
 66.         test [`i'_3_2a_mean]hotline=[`i'_3_3a_mean]hotline      
 67.         scalar define t_`i'_3_6=r(p)
 68.         display t_`i'_3_6
 69.         test [`i'_3_2a_mean]verdade=[`i'_3_3a_mean]verdade
 70.         scalar define t_`i'_3_7=r(p)
 71.         display t_`i'_3_7
 72.         
.         global list1="$list1" + " `i'_2_1" + " `i'_3_1" + " `i'_2_2" + " `i'_3_2"  + " `i'_2_3
> " + " `i'_3_3"
 73.         
.         }

Linear regression                                      Number of obs =    1119
                                                       F(  6,   160) =    4.69
                                                       Prob > F      =  0.0002
                                                       R-squared     =  0.0259
                                                       Root MSE      =  1.0162

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zscfree~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0769148   .0817682    -0.94   0.348     -.238399    .0845694
     hotline |   .1713744   .0873489     1.96   0.052    -.0011312    .3438799
     verdade |  -.0093477   .0830468    -0.11   0.911    -.1733569    .1546615
         pr1 |   .3447882     .08463     4.07   0.000     .1776522    .5119242
         pr2 |   .2628195   .0811764     3.24   0.001      .102504     .423135
         pr3 |   .0806316    .090984     0.89   0.377    -.0990529    .2603161
       _cons |  -.1743565   .0675127    -2.58   0.011    -.3076875   -.0410256
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfree~2009 |       266    8.43e-08           1  -.5018768   4.101546
8.425e-08

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    6.82
            Prob > F =    0.0099
.00988158

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.57
            Prob > F =    0.4532
.45317872

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    3.58
            Prob > F =    0.0601
.06013363

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    2.40
            Prob > F =    0.0699
.06993099

Linear regression                                      Number of obs =     948
                                                       F(  6,   160) =    4.24
                                                       Prob > F      =  0.0006
                                                       R-squared     =  0.0241
                                                       Root MSE      =  1.0337

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zscfree~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0952473   .0841923    -1.13   0.260    -.2615187    .0710242
     hotline |   .1583031   .0914199     1.73   0.085    -.0222422    .3388484
     verdade |   .0525892   .0918562     0.57   0.568    -.1288178    .2339961
         pr1 |   .3211904   .0924313     3.47   0.001     .1386478    .5037331
         pr2 |   .2628868    .088945     2.96   0.004     .0872292    .4385444
         pr3 |   .0622791   .0986748     0.63   0.529    -.1325939     .257152
       _cons |  -.1637474    .071808    -2.28   0.024    -.3055612   -.0219336
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     948
-------------+------------------------------           F(  6,   941) =    3.87
       Model |  24.8300473     6  4.13834122           Prob > F      =  0.0008
    Residual |  1005.50359   941  1.06854792           R-squared     =  0.0241
-------------+------------------------------           Adj R-squared =  0.0179
       Total |  1030.33364   947  1.08799751           Root MSE      =  1.0337

------------------------------------------------------------------------------
zscfree~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0952473   .0920416    -1.03   0.301    -.2758779    .0853834
     hotline |   .1583031   .0935612     1.69   0.091    -.0253097     .341916
     verdade |   .0525892    .094725     0.56   0.579    -.1333075    .2384858
         pr1 |   .3211904   .0936692     3.43   0.001     .1373658    .5050151
         pr2 |   .2628868   .0948866     2.77   0.006      .076673    .4491006
         pr3 |   .0622791   .0953413     0.65   0.514     -.124827    .2493851
       _cons |  -.1637474   .0856016    -1.91   0.056    -.3317395    .0042447
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     437
                                                       F(  6,   150) =    4.34
                                                       Prob > F      =  0.0005
                                                       R-squared     =  0.0458
                                                       Root MSE      =  .95763

                                   (Std. Err. adjusted for 151 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zscfree~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0109206   .1312246     0.08   0.934    -.2483668     .270208
     hotline |   .2232004   .1528725     1.46   0.146    -.0788612     .525262
     verdade |   -.246242   .1081269    -2.28   0.024    -.4598906   -.0325934
         pr1 |   .4196858   .1225463     3.42   0.001     .1775459    .6618258
         pr2 |   .3277373    .108702     3.02   0.003     .1129525    .5425222
         pr3 |   .0839208   .1050313     0.80   0.426    -.1236112    .2914527
       _cons |  -.2107212    .072442    -2.91   0.004    -.3538598   -.0675826
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     437
-------------+------------------------------           F(  6,   430) =    3.44
       Model |  18.9131293     6  3.15218822           Prob > F      =  0.0025
    Residual |  394.334605   430  .917057221           R-squared     =  0.0458
-------------+------------------------------           Adj R-squared =  0.0325
       Total |  413.247734   436  .947815904           Root MSE      =  .95763

------------------------------------------------------------------------------
zscfree~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0109206   .1442721     0.08   0.940    -.2726456    .2944868
     hotline |   .2232004   .1354245     1.65   0.100    -.0429759    .4893767
     verdade |   -.246242    .140976    -1.75   0.081    -.5233298    .0308458
         pr1 |   .4196858   .1275906     3.29   0.001     .1689069    .6704647
         pr2 |   .3277373   .1305401     2.51   0.012     .0711614    .5843133
         pr3 |   .0839208   .1276464     0.66   0.511    -.1669677    .3348092
       _cons |  -.2107212   .0973991    -2.16   0.031    -.4021588   -.0192837
------------------------------------------------------------------------------

Simultaneous results for zscfreefair2009_2_2a, zscfreefair2009_2_3a

                                                  Number of obs   =       1119

                                                 (Std. Err. adjusted for 161 clusters in ea)
--------------------------------------------------------------------------------------------
                           |               Robust
                           |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
zscfreefair2009_2_2a_mean  |
                 civiceduc |  -.0952473   .0839251    -1.13   0.256    -.2597375     .069243
                   hotline |   .1583031   .0911298     1.74   0.082     -.020308    .3369143
                   verdade |   .0525892   .0915647     0.57   0.566    -.1268744    .2320528
                       pr1 |   .3211904    .092138     3.49   0.000     .1406033    .5017776
                       pr2 |   .2628868   .0886628     2.97   0.003      .089111    .4366627
                       pr3 |   .0622791   .0983617     0.63   0.527    -.1305063    .2550644
                     _cons |  -.1637474   .0715802    -2.29   0.022     -.304042   -.0234528
---------------------------+----------------------------------------------------------------
zscfreefair2009_2_2a_lnvar |
                     _cons |   .0663006   .0869356     0.76   0.446      -.10409    .2366913
---------------------------+----------------------------------------------------------------
zscfreefair2009_2_3a_mean  |
                 civiceduc |   .0109206   .1302916     0.08   0.933    -.2444462    .2662874
                   hotline |   .2232004   .1517856     1.47   0.141    -.0742939    .5206946
                   verdade |   -.246242   .1073581    -2.29   0.022    -.4566601   -.0358239
                       pr1 |   .4196858    .121675     3.45   0.001     .1812072    .6581645
                       pr2 |   .3277373   .1079291     3.04   0.002     .1162002    .5392745
                       pr3 |   .0839208   .1042845     0.80   0.421    -.1204732    .2883147
                     _cons |  -.2107212   .0719269    -2.93   0.003    -.3516954    -.069747
---------------------------+----------------------------------------------------------------
zscfreefair2009_2_3a_lnvar |
                     _cons |  -.0865854   .1187355    -0.73   0.466    -.3193026    .1461318
--------------------------------------------------------------------------------------------

 ( 1)  [zscfreefair2009_2_2a_mean]civiceduc - [zscfreefair2009_2_3a_mean]civiceduc = 0

           chi2(  1) =    0.75
         Prob > chi2 =    0.3864
.38642228

 ( 1)  [zscfreefair2009_2_2a_mean]hotline - [zscfreefair2009_2_3a_mean]hotline = 0

           chi2(  1) =    0.18
         Prob > chi2 =    0.6720
.67203587

 ( 1)  [zscfreefair2009_2_2a_mean]verdade - [zscfreefair2009_2_3a_mean]verdade = 0

           chi2(  1) =    6.76
         Prob > chi2 =    0.0093
.00930813

Linear regression                                      Number of obs =    1104
                                                       F( 25,   160) =    3.18
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0488
                                                       Root MSE      =  1.0141

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zscfree~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0434465   .0850473    -0.51   0.610    -.2114065    .1245135
     hotline |    .193046   .0841946     2.29   0.023     .0267699    .3593221
     verdade |   .0151687   .0837925     0.18   0.857    -.1503133    .1806507
         pr1 |    .288803   .1022156     2.83   0.005     .0869372    .4906688
         pr2 |   .3321618   .0990608     3.35   0.001     .1365265    .5277971
         pr3 |   .1049907   .1076095     0.98   0.331    -.1075275    .3175089
        post |  -.0251029   .1046546    -0.24   0.811    -.2317855    .1815796
   post_miss |  -.0557102   .1467344    -0.38   0.705    -.3454962    .2340757
      health |   .1149198   .0704384     1.63   0.105    -.0241891    .2540288
 health_miss |  -.2271179   .1545449    -1.47   0.144    -.5323289    .0780931
         sex |   .1666337   .0637339     2.61   0.010     .0407656    .2925017
         age |  -.0013806   .0028353    -0.49   0.627    -.0069801    .0042188
      single |   .0947498   .0750556     1.26   0.209    -.0534776    .2429773
       divor |   .2067888    .553556     0.37   0.709    -.8864297    1.300007
     protest |  -.0096003   .0706089    -0.14   0.892    -.1490459    .1298454
         com |  -.1833761   .1110756    -1.65   0.101    -.4027394    .0359873
        prof |   .0520353   .2214398     0.23   0.815    -.3852864    .4893571
         tea |  -.0121692   .1209461    -0.10   0.920    -.2510259    .2266875
     comform |   .0287047   .2837536     0.10   0.920    -.5316807    .5890901
         dom |  -.0938124   .0824712    -1.14   0.257    -.2566849    .0690601
    econfood |  -.0164306   .0244155    -0.67   0.502    -.0646489    .0317877
       house |   .0198945   .0830257     0.24   0.811    -.1440731     .183862
      llomue |   .2187448   .1324332     1.65   0.101    -.0427977    .4802874
     chitsua |     -.1673   .1871799    -0.89   0.373     -.536962    .2023619
      living |    .002328   .0288001     0.08   0.936    -.0545493    .0592053
       _cons |  -.3140312   .1563165    -2.01   0.046     -.622741   -.0053215
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscfree~2009 |       264    .0038022    1.002833  -.5018768   4.101546
.00380218

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    5.78
            Prob > F =    0.0174
.01737174

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.40
            Prob > F =    0.5265
.52650116

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    3.48
            Prob > F =    0.0640
.06399892

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    2.36
            Prob > F =    0.0734
.07342215

Linear regression                                      Number of obs =     938
                                                       F( 25,   160) =    4.37
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0530
                                                       Root MSE      =  1.0322

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zscfree~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0317394   .0854354    -0.37   0.711    -.2004659    .1369872
     hotline |   .1868004   .0878289     2.13   0.035     .0133471    .3602538
     verdade |   .0780282   .0903685     0.86   0.389    -.1004407    .2564972
         pr1 |   .2571238   .1140708     2.25   0.026     .0318451    .4824024
         pr2 |   .3488806   .1074743     3.25   0.001     .1366295    .5611317
         pr3 |   .0976793      .1179     0.83   0.409    -.1351615    .3305202
        post |  -.0103928    .117226    -0.09   0.929    -.2419027    .2211171
   post_miss |   .0208578   .1421896     0.15   0.884    -.2599527    .3016683
      health |   .0858007   .0735268     1.17   0.245    -.0594075     .231009
 health_miss |  -.3179271   .1389784    -2.29   0.023    -.5923958   -.0434584
         sex |   .1965728   .0722059     2.72   0.007     .0539733    .3391722
         age |  -.0014395   .0030651    -0.47   0.639    -.0074928    .0046138
      single |   .1455822   .0848147     1.72   0.088    -.0219185    .3130829
       divor |    -.42346   .1315703    -3.22   0.002    -.6832985   -.1636215
     protest |  -.0387442   .0809371    -0.48   0.633    -.1985869    .1210985
         com |  -.1717413   .1227517    -1.40   0.164    -.4141639    .0706813
        prof |   .0825073   .2517662     0.33   0.744    -.4147062    .5797208
         tea |   .0437144   .1395449     0.31   0.754     -.231873    .3193018
     comform |   .0500705   .3373493     0.15   0.882    -.6161611    .7163022
         dom |  -.1173907   .0948161    -1.24   0.217    -.3046432    .0698618
    econfood |  -.0167966   .0265539    -0.63   0.528    -.0692378    .0356447
       house |   .0235548   .0913529     0.26   0.797    -.1568583    .2039679
      llomue |   .2316288   .1324339     1.75   0.082    -.0299151    .4931727
     chitsua |  -.0246569   .2259602    -0.11   0.913    -.4709059    .4215922
      living |   .0091817   .0312744     0.29   0.769    -.0525821    .0709455
       _cons |  -.3301069   .1617246    -2.04   0.043     -.649497   -.0107168
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     938
-------------+------------------------------           F( 25,   912) =    2.04
       Model |  54.3366365    25  2.17346546           Prob > F      =  0.0020
    Residual |  971.744176   912  1.06550896           R-squared     =  0.0530
-------------+------------------------------           Adj R-squared =  0.0270
       Total |  1026.08081   937  1.09507024           Root MSE      =  1.0322

------------------------------------------------------------------------------
zscfree~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0317394    .096904    -0.33   0.743    -.2219201    .1584413
     hotline |   .1868004   .0964091     1.94   0.053     -.002409    .3760098
     verdade |   .0780282   .0983475     0.79   0.428    -.1149856     .271042
         pr1 |   .2571238    .115415     2.23   0.026     .0306139    .4836337
         pr2 |   .3488806   .1133744     3.08   0.002     .1263755    .5713857
         pr3 |   .0976793   .1103837     0.88   0.376    -.1189562    .3143148
        post |  -.0103928   .1176334    -0.09   0.930    -.2412565    .2204709
   post_miss |   .0208578   .1968633     0.11   0.916    -.3654999    .4072155
      health |   .0858007    .080705     1.06   0.288    -.0725883    .2441898
 health_miss |  -.3179271    .220107    -1.44   0.149    -.7499021    .1140479
         sex |   .1965728   .0724441     2.71   0.007     .0543963    .3387493
         age |  -.0014395   .0028294    -0.51   0.611    -.0069924    .0041134
      single |   .1455822   .0936165     1.56   0.120    -.0381465     .329311
       divor |    -.42346   .4301605    -0.98   0.325     -1.26768    .4207595
     protest |  -.0387442   .0832453    -0.47   0.642    -.2021188    .1246304
         com |  -.1717413   .1591665    -1.08   0.281    -.4841166    .1406339
        prof |   .0825073   .2729846     0.30   0.763    -.4532436    .6182582
         tea |   .0437144   .1661506     0.26   0.793    -.2823675    .3697962
     comform |   .0500705   .3341654     0.15   0.881     -.605752    .7058931
         dom |  -.1173907   .1064374    -1.10   0.270    -.3262814       .0915
    econfood |  -.0167966   .0301612    -0.56   0.578    -.0759901    .0423969
       house |   .0235548   .1007328     0.23   0.815    -.1741402    .2212497
      llomue |   .2316288    .135982     1.70   0.089    -.0352452    .4985028
     chitsua |  -.0246569   .3357939    -0.07   0.941    -.6836753    .6343616
      living |   .0091817    .034064     0.27   0.788    -.0576713    .0760347
       _cons |  -.3301069   .1880624    -1.76   0.080    -.6991923    .0389785
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     430
                                                       F( 25,   149) =    2.48
                                                       Prob > F      =  0.0004
                                                       R-squared     =  0.0849
                                                       Root MSE      =  .95598

                                   (Std. Err. adjusted for 150 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zscfree~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0252969   .1413506     0.18   0.858    -.2540138    .3046076
     hotline |   .2058525   .1484933     1.39   0.168    -.0875723    .4992772
     verdade |  -.2059035   .1208798    -1.70   0.091    -.4447636    .0329565
         pr1 |   .4569932   .1616665     2.83   0.005     .1375379    .7764484
         pr2 |    .385983   .1394086     2.77   0.006     .1105097    .6614563
         pr3 |   .0977612   .1148108     0.85   0.396    -.1291065    .3246288
        post |   .0120592   .1350227     0.09   0.929    -.2547474    .2788658
   post_miss |   .0925591    .177924     0.52   0.604    -.2590211    .4441392
      health |   .2332105   .0974788     2.39   0.018      .040591    .4258299
 health_miss |  -.1594707   .2133414    -0.75   0.456    -.5810361    .2620948
         sex |   .1963972   .0945295     2.08   0.039     .0096056    .3831887
         age |  -.0069909   .0040559    -1.72   0.087    -.0150054    .0010235
      single |   .0147513    .104763     0.14   0.888    -.1922619    .2217644
       divor |   1.226166   1.355533     0.90   0.367    -1.452384    3.904716
     protest |   .0572582    .106156     0.54   0.590    -.1525075    .2670239
         com |  -.2105923   .1879668    -1.12   0.264    -.5820172    .1608326
        prof |  -.1533326   .2215132    -0.69   0.490    -.5910457    .2843804
         tea |  -.0383194   .1993308    -0.19   0.848    -.4321998    .3555609
     comform |   .2910799    .356835     0.82   0.416    -.4140308    .9961905
         dom |   .0609093   .1670047     0.36   0.716    -.2690942    .3909128
    econfood |   .0097612   .0402374     0.24   0.809    -.0697485    .0892709
       house |  -.0317764   .1467125    -0.22   0.829    -.3216823    .2581295
      llomue |   .0825675   .1834732     0.45   0.653    -.2799779     .445113
     chitsua |  -.2197615   .3530194    -0.62   0.535    -.9173324    .4778095
      living |   .0314554   .0450443     0.70   0.486    -.0575527    .1204635
       _cons |  -.3596293    .224164    -1.60   0.111    -.8025804    .0833217
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     430
-------------+------------------------------           F( 25,   404) =    1.50
       Model |  34.2486268    25  1.36994507           Prob > F      =  0.0597
    Residual |  369.216738   404  .913902818           R-squared     =  0.0849
-------------+------------------------------           Adj R-squared =  0.0283
       Total |  403.465365   429  .940478707           Root MSE      =  .95598

------------------------------------------------------------------------------
zscfree~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0252969   .1521035     0.17   0.868    -.2737162      .32431
     hotline |   .2058525   .1421568     1.45   0.148     -.073607     .485312
     verdade |  -.2059035   .1497718    -1.37   0.170    -.5003328    .0885258
         pr1 |   .4569932   .1621724     2.82   0.005      .138186    .7758004
         pr2 |    .385983   .1562173     2.47   0.014     .0788828    .6930833
         pr3 |   .0977612   .1532599     0.64   0.524    -.2035253    .3990477
        post |   .0120592    .139505     0.09   0.931    -.2621871    .2863055
   post_miss |   .0925591   .2315564     0.40   0.690    -.3626468    .5477649
      health |   .2332105   .1175996     1.98   0.048      .002027     .464394
 health_miss |  -.1594707   .3338244    -0.48   0.633    -.8157205    .4967791
         sex |   .1963972   .0995917     1.97   0.049     .0006145    .3921798
         age |  -.0069909   .0040483    -1.73   0.085    -.0149494    .0009675
      single |   .0147513   .1217477     0.12   0.904    -.2245869    .2540894
       divor |   1.226166   .5630158     2.18   0.030     .1193596    2.332972
     protest |   .0572582   .1190104     0.48   0.631    -.1766988    .2912153
         com |  -.2105923   .2416937    -0.87   0.384    -.6857266     .264542
        prof |  -.1533326   .3302873    -0.46   0.643     -.802629    .4959637
         tea |  -.0383194     .20188    -0.19   0.850    -.4351858    .3585469
     comform |   .2910799   .4020839     0.72   0.470     -.499358    1.081518
         dom |   .0609093   .1478154     0.41   0.681    -.2296741    .3514927
    econfood |   .0097612   .0435122     0.22   0.823    -.0757774    .0952997
       house |  -.0317764   .1388457    -0.23   0.819    -.3047267     .241174
      llomue |   .0825675   .1928883     0.43   0.669    -.2966225    .4617576
     chitsua |  -.2197615   .5014904    -0.44   0.661    -1.205618    .7660952
      living |   .0314554   .0457886     0.69   0.492    -.0585583    .1214692
       _cons |  -.3596293   .2553998    -1.41   0.160    -.8617078    .1424491
------------------------------------------------------------------------------

Simultaneous results for zscfreefair2009_3_2a, zscfreefair2009_3_3a

                                                  Number of obs   =       1104

                                                 (Std. Err. adjusted for 161 clusters in ea)
--------------------------------------------------------------------------------------------
                           |               Robust
                           |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
zscfreefair2009_3_2a_mean  |
                 civiceduc |  -.0317394    .084288    -0.38   0.707    -.1969408     .133462
                   hotline |   .1868004   .0866493     2.16   0.031      .016971    .3566299
                   verdade |   .0780282   .0891548     0.88   0.381     -.096712    .2527684
                       pr1 |   .2571238   .1125388     2.28   0.022     .0365518    .4776957
                       pr2 |   .3488806   .1060308     3.29   0.001      .141064    .5566972
                       pr3 |   .0976793   .1163165     0.84   0.401    -.1302968    .3256555
                      post |  -.0103928   .1156516    -0.09   0.928    -.2370658    .2162802
                 post_miss |   .0208578   .1402799     0.15   0.882    -.2540858    .2958014
                    health |   .0858007   .0725393     1.18   0.237    -.0563737    .2279752
               health_miss |  -.3179271   .1371119    -2.32   0.020    -.5866614   -.0491928
                       sex |   .1965728   .0712361     2.76   0.006     .0569526    .3361929
                       age |  -.0014395   .0030239    -0.48   0.634    -.0073663    .0044873
                    single |   .1455822   .0836756     1.74   0.082    -.0184189    .3095834
                     divor |    -.42346   .1298033    -3.26   0.001    -.6778697   -.1690502
                   protest |  -.0387442     .07985    -0.49   0.628    -.1952474     .117759
                       com |  -.1717413   .1211031    -1.42   0.156     -.409099    .0656164
                      prof |   .0825073   .2483848     0.33   0.740     -.404318    .5693327
                       tea |   .0437144   .1376707     0.32   0.751    -.2261153     .313544
                   comform |   .0500705   .3328185     0.15   0.880    -.6022417    .7023827
                       dom |  -.1173907   .0935427    -1.25   0.209    -.3007309    .0659495
                  econfood |  -.0167966   .0261972    -0.64   0.521    -.0681422     .034549
                     house |   .0235548    .090126     0.26   0.794    -.1530889    .2001985
                    llomue |   .2316288   .1306552     1.77   0.076    -.0244507    .4877083
                   chitsua |  -.0246569   .2229254    -0.11   0.912    -.4615826    .4122688
                    living |   .0091817   .0308543     0.30   0.766    -.0512917    .0696551
                     _cons |  -.3301069   .1595525    -2.07   0.039    -.6428241   -.0173897
---------------------------+----------------------------------------------------------------
zscfreefair2009_3_2a_lnvar |
                     _cons |   .0634526   .0871194     0.73   0.466    -.1072984    .2342035
---------------------------+----------------------------------------------------------------
zscfreefair2009_3_3a_mean  |
                 civiceduc |   .0252969   .1371388     0.18   0.854    -.2434901     .294084
                   hotline |   .2058525   .1440686     1.43   0.153    -.0765168    .4882218
                   verdade |  -.2059035   .1172779    -1.76   0.079     -.435764    .0239569
                       pr1 |   .4569932   .1568493     2.91   0.004     .1495741    .7644122
                       pr2 |    .385983   .1352546     2.85   0.004     .1208888    .6510772
                       pr3 |   .0977612   .1113898     0.88   0.380    -.1205587    .3160811
                      post |   .0120592   .1309994     0.09   0.927    -.2446949    .2688132
                 post_miss |   .0925591   .1726223     0.54   0.592    -.2457745    .4308926
                    health |   .2332105   .0945742     2.47   0.014     .0478484    .4185726
               health_miss |  -.1594707   .2069844    -0.77   0.441    -.5651527    .2462113
                       sex |   .1963972   .0917128     2.14   0.032     .0166434    .3761509
                       age |  -.0069909    .003935    -1.78   0.076    -.0147035    .0007216
                    single |   .0147513   .1016414     0.15   0.885    -.1844622    .2139647
                     divor |   1.226166   1.315141     0.93   0.351    -1.351464    3.803796
                   protest |   .0572582   .1029929     0.56   0.578    -.1446041    .2591205
                       com |  -.2105923   .1823659    -1.15   0.248    -.5680229    .1468383
                      prof |  -.1533326   .2149127    -0.71   0.476    -.5745538    .2678886
                       tea |  -.0383194   .1933913    -0.20   0.843    -.4173594    .3407206
                   comform |   .2910799   .3462023     0.84   0.400    -.3874641    .9696239
                       dom |   .0609093   .1620284     0.38   0.707    -.2566606    .3784792
                  econfood |   .0097612   .0390385     0.25   0.803    -.0667528    .0862752
                     house |  -.0317764   .1423409    -0.22   0.823    -.3107594    .2472066
                    llomue |   .0825675   .1780062     0.46   0.643    -.2663182    .4314533
                   chitsua |  -.2197615   .3425004    -0.64   0.521    -.8910499    .4515269
                    living |   .0314554   .0437021     0.72   0.472    -.0541991      .11711
                     _cons |  -.3596293   .2174846    -1.65   0.098    -.7858912    .0666325
---------------------------+----------------------------------------------------------------
zscfreefair2009_3_3a_lnvar |
                     _cons |   -.090031   .1137653    -0.79   0.429    -.3130069    .1329448
--------------------------------------------------------------------------------------------

 ( 1)  [zscfreefair2009_3_2a_mean]civiceduc - [zscfreefair2009_3_3a_mean]civiceduc = 0

           chi2(  1) =    0.20
         Prob > chi2 =    0.6552
.65520202

 ( 1)  [zscfreefair2009_3_2a_mean]hotline - [zscfreefair2009_3_3a_mean]hotline = 0

           chi2(  1) =    0.02
         Prob > chi2 =    0.8963
.89628445

 ( 1)  [zscfreefair2009_3_2a_mean]verdade - [zscfreefair2009_3_3a_mean]verdade = 0

           chi2(  1) =    5.32
         Prob > chi2 =    0.0211
.02112791

. 
. matrix define means=(m_zscfreefair2009_2_1, m_zscfreefair2009_3_1 \ t_zscfreefair2009_2_1_1, t
> _zscfreefair2009_3_1_1 \ t_zscfreefair2009_2_1_2, t_zscfreefair2009_3_1_2 \ t_zscfreefair2009_
> 2_1_3, t_zscfreefair2009_3_1_3 \ t_zscfreefair2009_2_1_4, t_zscfreefair2009_3_1_4 \ t_zscfreef
> air2009_2_5, t_zscfreefair2009_3_5 \ t_zscfreefair2009_2_6, t_zscfreefair2009_3_6 \ t_zscfreef
> air2009_2_7, t_zscfreefair2009_3_7)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_survey.xml") append sheet("freefa
> ir2009") 


note: results saved to outputregs_survey.xml

. xml_tab $list2, save("outputregs_survey.xml") append sheet("freefair2009 stats") 


note: results saved to outputregs_survey.xml

. estimates clear

. 
. *vcount2009
. 
. global final="zscvcount2009"

. 
. global list1=""

. global list2=""

. 
. foreach i in $final {
  2. 
.         regress `i' $treat $prov if time==1, cluster(ea)
  3.         estimates store `i'_2_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2_1=r(mean)
  6.         display m_`i'_2_1
  7.         test civiceduc = hotline
  8.         scalar define t_`i'_2_1_1=r(p)
  9.         display t_`i'_2_1_1
 10.         test civiceduc = verdade
 11.         scalar define t_`i'_2_1_2=r(p)
 12.         display t_`i'_2_1_2
 13.         test hotline = verdade
 14.         scalar define t_`i'_2_1_3=r(p)
 15.         display t_`i'_2_1_3
 16.         test civiceduc hotline verdade
 17.         scalar define t_`i'_2_1_4=r(p)
 18.         display t_`i'_2_1_4
 19. 
.         regress `i' $treat $prov if time==1 & lazy==0, cluster(ea)
 20.         estimates store `i'_2_2
 21.         regress `i' $treat $prov if time==1 & lazy==0
 22.         estimates store `i'_2_2a
 23.         
.         regress `i' $treat $prov if time==1 & (lazy==1|control==1), cluster(ea)
 24.         estimates store `i'_2_3
 25.         regress `i' $treat $prov if time==1 & (lazy==1|control==1)
 26.         estimates store `i'_2_3a
 27. 
.         suest `i'_2_2a `i'_2_3a, cluster(ea)
 28.         test [`i'_2_2a_mean]civiceduc=[`i'_2_3a_mean]civiceduc  
 29.         scalar define t_`i'_2_5=r(p)
 30.         display t_`i'_2_5
 31.         test [`i'_2_2a_mean]hotline=[`i'_2_3a_mean]hotline      
 32.         scalar define t_`i'_2_6=r(p)
 33.         display t_`i'_2_6
 34.         test [`i'_2_2a_mean]verdade=[`i'_2_3a_mean]verdade
 35.         scalar define t_`i'_2_7=r(p)
 36.         display t_`i'_2_7
 37. 
.         regress `i' $treat $prov $ea $controls if time==1, cluster(ea)
 38.         estimates store `i'_3_1
 39.         sum `i' if e(sample) & control == 1
 40.         scalar define m_`i'_3_1=r(mean)
 41.         display m_`i'_3_1
 42.         test civiceduc = hotline
 43.         scalar define t_`i'_3_1_1=r(p)
 44.         display t_`i'_3_1_1
 45.         test civiceduc = verdade
 46.         scalar define t_`i'_3_1_2=r(p)
 47.         display t_`i'_3_1_2
 48.         test hotline = verdade
 49.         scalar define t_`i'_3_1_3=r(p)
 50.         display t_`i'_3_1_3
 51.         test civiceduc hotline verdade
 52.         scalar define t_`i'_3_1_4=r(p)
 53.         display t_`i'_3_1_4
 54. 
.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0, cluster(ea)
 55.         estimates store `i'_3_2
 56.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0
 57.         estimates store `i'_3_2a
 58.         
.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1), cluster(ea)
 59.         estimates store `i'_3_3
 60.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1)
 61.         estimates store `i'_3_3a
 62. 
.         suest `i'_3_2a `i'_3_3a, cluster(ea)
 63.         test [`i'_3_2a_mean]civiceduc=[`i'_3_3a_mean]civiceduc  
 64.         scalar define t_`i'_3_5=r(p)
 65.         display t_`i'_3_5
 66.         test [`i'_3_2a_mean]hotline=[`i'_3_3a_mean]hotline      
 67.         scalar define t_`i'_3_6=r(p)
 68.         display t_`i'_3_6
 69.         test [`i'_3_2a_mean]verdade=[`i'_3_3a_mean]verdade
 70.         scalar define t_`i'_3_7=r(p)
 71.         display t_`i'_3_7
 72.         
.         global list1="$list1" + " `i'_2_1" + " `i'_3_1" + " `i'_2_2" + " `i'_3_2"  + " `i'_2_3
> " + " `i'_3_3"
 73.         
.         }

Linear regression                                      Number of obs =    1102
                                                       F(  6,   160) =    4.77
                                                       Prob > F      =  0.0002
                                                       R-squared     =  0.0213
                                                       Root MSE      =  .95611

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zscvcou~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1388385   .0757488    -1.83   0.069    -.2884349     .010758
     hotline |  -.0203448   .0791706    -0.26   0.798    -.1766989    .1360093
     verdade |  -.0792254   .0801084    -0.99   0.324    -.2374316    .0789807
         pr1 |   .3364235   .0746727     4.51   0.000     .1889522    .4838948
         pr2 |   .0475436    .070227     0.68   0.499    -.0911478     .186235
         pr3 |   .1518678   .0594377     2.56   0.012     .0344843    .2692514
       _cons |  -.1366082   .0664824    -2.05   0.042    -.2679045    -.005312
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscvcou~2009 |       263    1.70e-07           1  -.5391424   4.407177
1.700e-07

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    3.25
            Prob > F =    0.0732
.0731757

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.80
            Prob > F =    0.3716
.37160133

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.70
            Prob > F =    0.4046
.40456944

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.61
            Prob > F =    0.1896
.1895725

Linear regression                                      Number of obs =     935
                                                       F(  6,   160) =    3.47
                                                       Prob > F      =  0.0030
                                                       R-squared     =  0.0189
                                                       Root MSE      =   .9706

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zscvcou~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1431714   .0798791    -1.79   0.075    -.3009248     .014582
     hotline |  -.0439127   .0861119    -0.51   0.611    -.2139752    .1261499
     verdade |  -.0415877   .0880603    -0.47   0.637    -.2154981    .1323228
         pr1 |    .314262   .0818337     3.84   0.000     .1526484    .4758756
         pr2 |    .038286   .0837708     0.46   0.648     -.127153    .2037251
         pr3 |   .1334074   .0744273     1.79   0.075    -.0135792    .2803939
       _cons |  -.1240841   .0699885    -1.77   0.078    -.2623046    .0141364
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     935
-------------+------------------------------           F(  6,   928) =    2.98
       Model |  16.8422782     6  2.80704637           Prob > F      =  0.0069
    Residual |  874.239119   928  .942068017           R-squared     =  0.0189
-------------+------------------------------           Adj R-squared =  0.0126
       Total |  891.081398   934  .954048606           Root MSE      =   .9706

------------------------------------------------------------------------------
zscvcou~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1431714   .0868448    -1.65   0.100    -.3136064    .0272636
     hotline |  -.0439127     .08862    -0.50   0.620    -.2178315    .1300062
     verdade |  -.0415877   .0895261    -0.46   0.642    -.2172848    .1341095
         pr1 |    .314262   .0884901     3.55   0.000     .1405981    .4879259
         pr2 |    .038286    .089875     0.43   0.670    -.1380957    .2146678
         pr3 |   .1334074   .0898157     1.49   0.138    -.0428581    .3096728
       _cons |  -.1240841   .0806693    -1.54   0.124    -.2823994    .0342312
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     430
                                                       F(  6,   148) =    2.92
                                                       Prob > F      =  0.0102
                                                       R-squared     =  0.0210
                                                       Root MSE      =  .95542

                                   (Std. Err. adjusted for 149 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zscvcou~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.126375    .137852    -0.92   0.361    -.3987875    .1460374
     hotline |   .0584108    .128805     0.45   0.651    -.1961237    .3129453
     verdade |   -.223612    .133232    -1.68   0.095    -.4868947    .0396707
         pr1 |   .2316839   .1196107     1.94   0.055    -.0046815    .4680493
         pr2 |   .1058401   .1304044     0.81   0.418    -.1518549    .3635352
         pr3 |   .2611338   .1034356     2.52   0.013     .0567323    .4655353
       _cons |   -.149683   .0758338    -1.97   0.050    -.2995398    .0001738
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     430
-------------+------------------------------           F(  6,   423) =    1.51
       Model |  8.29363489     6  1.38227248           Prob > F      =  0.1718
    Residual |  386.128664   423  .912833721           R-squared     =  0.0210
-------------+------------------------------           Adj R-squared =  0.0071
       Total |  394.422299   429  .919399298           Root MSE      =  .95542

------------------------------------------------------------------------------
zscvcou~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.126375   .1464083    -0.86   0.389    -.4141534    .1614034
     hotline |   .0584108   .1359597     0.43   0.668    -.2088299    .3256515
     verdade |   -.223612   .1418277    -1.58   0.116    -.5023868    .0551629
         pr1 |   .2316839   .1281574     1.81   0.071    -.0202208    .4835886
         pr2 |   .1058401   .1311118     0.81   0.420    -.1518716    .3635519
         pr3 |   .2611338   .1282482     2.04   0.042     .0090506     .513217
       _cons |   -.149683    .097491    -1.54   0.125    -.3413102    .0419442
------------------------------------------------------------------------------

Simultaneous results for zscvcount2009_2_2a, zscvcount2009_2_3a

                                                  Number of obs   =       1102

                                               (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------------------
                         |               Robust
                         |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
zscvcount2009_2_2a_mean  |
               civiceduc |  -.1431714   .0796221    -1.80   0.072    -.2992279    .0128851
                 hotline |  -.0439127   .0858349    -0.51   0.609    -.2121459    .1243206
                 verdade |  -.0415877    .087777    -0.47   0.636    -.2136274    .1304521
                     pr1 |    .314262   .0815705     3.85   0.000     .1543868    .4741372
                     pr2 |    .038286   .0835013     0.46   0.647    -.1253735    .2019456
                     pr3 |   .1334074   .0741878     1.80   0.072    -.0119981    .2788129
                   _cons |  -.1240841   .0697634    -1.78   0.075    -.2608178    .0126496
-------------------------+----------------------------------------------------------------
zscvcount2009_2_2a_lnvar |
                   _cons |  -.0596778   .0923125    -0.65   0.518     -.240607    .1212514
-------------------------+----------------------------------------------------------------
zscvcount2009_2_3a_mean  |
               civiceduc |   -.126375   .1368502    -0.92   0.356    -.3945964    .1418464
                 hotline |   .0584108   .1278689     0.46   0.648    -.1922077    .3090293
                 verdade |   -.223612   .1322637    -1.69   0.091    -.4828441    .0356201
                     pr1 |   .2316839   .1187414     1.95   0.051     -.001045    .4644128
                     pr2 |   .1058401   .1294567     0.82   0.414    -.1478903    .3595706
                     pr3 |   .2611338   .1026839     2.54   0.011      .059877    .4623906
                   _cons |   -.149683   .0752826    -1.99   0.047    -.2972343   -.0021318
-------------------------+----------------------------------------------------------------
zscvcount2009_2_3a_lnvar |
                   _cons |  -.0912015   .1298496    -0.70   0.482     -.345702    .1632989
------------------------------------------------------------------------------------------

 ( 1)  [zscvcount2009_2_2a_mean]civiceduc - [zscvcount2009_2_3a_mean]civiceduc = 0

           chi2(  1) =    0.01
         Prob > chi2 =    0.9038
.90377973

 ( 1)  [zscvcount2009_2_2a_mean]hotline - [zscvcount2009_2_3a_mean]hotline = 0

           chi2(  1) =    0.56
         Prob > chi2 =    0.4544
.45443217

 ( 1)  [zscvcount2009_2_2a_mean]verdade - [zscvcount2009_2_3a_mean]verdade = 0

           chi2(  1) =    1.57
         Prob > chi2 =    0.2102
.21018608

Linear regression                                      Number of obs =    1088
                                                       F( 25,   160) =    3.37
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0375
                                                       Root MSE      =  .94677

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zscvcou~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1387752   .0814533    -1.70   0.090    -.2996375    .0220871
     hotline |  -.0333278   .0844612    -0.39   0.694    -.2001305    .1334748
     verdade |  -.0813743   .0873987    -0.93   0.353    -.2539782    .0912296
         pr1 |    .387418   .1115113     3.47   0.001     .1671941    .6076419
         pr2 |   .1130641   .0881927     1.28   0.202    -.0611077    .2872359
         pr3 |    .154094   .0816066     1.89   0.061    -.0070711     .315259
        post |  -.0456929    .097231    -0.47   0.639    -.2377145    .1463287
   post_miss |   .0569997   .0980012     0.58   0.562     -.136543    .2505424
      health |   .0510277    .060023     0.85   0.397    -.0675118    .1695672
 health_miss |  -.2291232   .0809031    -2.83   0.005    -.3888987   -.0693476
         sex |   .0456566   .0545989     0.84   0.404    -.0621709    .1534841
         age |  -.0035322   .0026753    -1.32   0.189    -.0088156    .0017512
      single |  -.0244688   .0777645    -0.31   0.753    -.1780461    .1291084
       divor |   .0722128   .3082926     0.23   0.815    -.5366348    .6810603
     protest |    .028398   .0727241     0.39   0.697    -.1152249    .1720209
         com |  -.1811963   .0934012    -1.94   0.054    -.3656544    .0032618
        prof |   .1665779   .3019988     0.55   0.582    -.4298399    .7629957
         tea |  -.1430028   .1162294    -1.23   0.220    -.3725445    .0865389
     comform |   .1697512    .282552     0.60   0.549    -.3882613    .7277637
         dom |  -.1088683   .0758578    -1.44   0.153      -.25868    .0409434
    econfood |   .0254359   .0247263     1.03   0.305    -.0233962     .074268
       house |   .1512854   .0657033     2.30   0.023     .0215279    .2810429
      llomue |  -.1008397    .154406    -0.65   0.515    -.4057765     .204097
     chitsua |   .1979192   .2440398     0.81   0.419    -.2840354    .6798739
      living |  -.0022161   .0296361    -0.07   0.940    -.0607445    .0563123
       _cons |   -.200346   .1408883    -1.42   0.157    -.4785866    .0778946
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zscvcou~2009 |       261   -.0053442     .996072  -.5391424   4.407177
-.00534418

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    2.64
            Prob > F =    0.1059
.10588699

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    0.69
            Prob > F =    0.4077
.40773656

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.45
            Prob > F =    0.5032
.50323279

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.37
            Prob > F =    0.2525
.2525022

Linear regression                                      Number of obs =     925
                                                       F( 25,   160) =    3.37
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0361
                                                       Root MSE      =  .97238

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zscvcou~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.145953   .0861257    -1.69   0.092    -.3160428    .0241369
     hotline |  -.0493528   .0909891    -0.54   0.588    -.2290473    .1303417
     verdade |  -.0554302   .0924745    -0.60   0.550    -.2380582    .1271978
         pr1 |   .3051494   .1134812     2.69   0.008     .0810352    .5292636
         pr2 |   .0901483   .1021041     0.88   0.379    -.1114972    .2917938
         pr3 |   .1165014   .0966303     1.21   0.230     -.074334    .3073368
        post |  -.0727173   .1092673    -0.67   0.507    -.2885095    .1430748
   post_miss |  -.0013967   .1374311    -0.01   0.992    -.2728096    .2700161
      health |   .0291521   .0682211     0.43   0.670    -.1055779    .1638821
 health_miss |  -.2553802   .1078983    -2.37   0.019    -.4684687   -.0422916
         sex |   .0203855   .0596006     0.34   0.733    -.0973199    .1380909
         age |  -.0030719   .0030285    -1.01   0.312    -.0090528     .002909
      single |    .031687   .0954627     0.33   0.740    -.1568424    .2202165
       divor |  -.2389447   .1792106    -1.33   0.184     -.592868    .1149785
     protest |   .0372174   .0781639     0.48   0.635    -.1171485    .1915834
         com |  -.2196827   .1025512    -2.14   0.034    -.4222112   -.0171542
        prof |   .3142541   .3585228     0.88   0.382    -.3937932    1.022301
         tea |  -.0620103   .1419328    -0.44   0.663    -.3423135    .2182929
     comform |   .1884057   .3241979     0.58   0.562    -.4518531    .8286646
         dom |  -.1509297   .0860469    -1.75   0.081     -.320864    .0190045
    econfood |   .0376589   .0283374     1.33   0.186    -.0183047    .0936225
       house |   .1305038   .0778242     1.68   0.096    -.0231914     .284199
      llomue |  -.0499059   .1659133    -0.30   0.764    -.3775683    .2777565
     chitsua |   .2021868    .309445     0.65   0.514    -.4089366    .8133102
      living |   -.009863   .0327194    -0.30   0.763    -.0744806    .0547546
       _cons |   -.129219   .1519713    -0.85   0.396    -.4293472    .1709093
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     925
-------------+------------------------------           F( 25,   899) =    1.35
       Model |  31.8150587    25  1.27260235           Prob > F      =  0.1201
    Residual |  850.029176   899  .945527449           R-squared     =  0.0361
-------------+------------------------------           Adj R-squared =  0.0093
       Total |  881.844235   924  .954376878           Root MSE      =  .97238

------------------------------------------------------------------------------
zscvcou~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.145953    .091591    -1.59   0.111    -.3257102    .0338042
     hotline |  -.0493528    .091558    -0.54   0.590    -.2290451    .1303396
     verdade |  -.0554302   .0932225    -0.59   0.552    -.2383892    .1275288
         pr1 |   .3051494    .109691     2.78   0.006     .0898691    .5204296
         pr2 |   .0901483   .1072804     0.84   0.401    -.1204009    .3006975
         pr3 |   .1165014   .1043148     1.12   0.264    -.0882274    .3212302
        post |  -.0727173   .1133283    -0.64   0.521    -.2951362    .1497016
   post_miss |  -.0013967   .1898355    -0.01   0.994    -.3739691    .3711757
      health |   .0291521   .0761117     0.38   0.702    -.1202252    .1785294
 health_miss |  -.2553802   .2130397    -1.20   0.231    -.6734932    .1627328
         sex |   .0203855   .0688786     0.30   0.767     -.114796     .155567
         age |  -.0030719   .0026684    -1.15   0.250    -.0083089     .002165
      single |    .031687   .0896466     0.35   0.724    -.1442539    .2076279
       divor |  -.2389447   .4053488    -0.59   0.556    -1.034485    .5565953
     protest |   .0372174   .0789858     0.47   0.638    -.1178007    .1922355
         com |  -.2196827   .1530074    -1.44   0.151     -.519976    .0806106
        prof |   .3142541   .2667429     1.18   0.239    -.2092573    .8377655
         tea |  -.0620103   .1621765    -0.38   0.702     -.380299    .2562784
     comform |   .1884057   .3009076     0.63   0.531    -.4021574    .7789689
         dom |  -.1509297    .100921    -1.50   0.135    -.3489979    .0471385
    econfood |   .0376589   .0286099     1.32   0.188    -.0184911    .0938089
       house |   .1305038    .094658     1.38   0.168    -.0552726    .3162802
      llomue |  -.0499059   .1289939    -0.39   0.699    -.3030702    .2032584
     chitsua |   .2021868   .3329831     0.61   0.544     -.451328    .8557016
      living |   -.009863    .032166    -0.31   0.759    -.0729922    .0532662
       _cons |   -.129219   .1774338    -0.73   0.467    -.4774516    .2190137
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     424
                                                       F( 25,   148) =    5.61
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0583
                                                       Root MSE      =  .93064

                                   (Std. Err. adjusted for 149 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zscvcou~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.109257   .1471846    -0.74   0.459    -.4001118    .1815978
     hotline |   .0268138   .1191302     0.23   0.822     -.208602    .2622296
     verdade |  -.1973577   .1490997    -1.32   0.188    -.4919971    .0972817
         pr1 |   .3288488   .1766883     1.86   0.065    -.0203089    .6780064
         pr2 |   .1538936   .1736444     0.89   0.377     -.189249    .4970363
         pr3 |   .2498482   .1313431     1.90   0.059    -.0097019    .5093983
        post |   .0459026    .153764     0.30   0.766     -.257954    .3497592
   post_miss |   .2089701    .088061     2.37   0.019     .0349508    .3829895
      health |   .1224604   .1045578     1.17   0.243    -.0841587    .3290794
 health_miss |  -.2014043   .1547923    -1.30   0.195    -.5072929    .1044843
         sex |   .0973254   .0987988     0.99   0.326     -.097913    .2925639
         age |  -.0065395   .0048636    -1.34   0.181    -.0161505    .0030715
      single |  -.1653535    .105993    -1.56   0.121    -.3748086    .0441016
       divor |   .4590567   .7438206     0.62   0.538    -1.010824    1.928937
     protest |   .0754461   .1295968     0.58   0.561    -.1806529    .3315452
         com |   .0111951   .2163815     0.05   0.959    -.4164012    .4387915
        prof |   -.304507   .2626742    -1.16   0.248    -.8235833    .2145694
         tea |  -.2461543   .1462663    -1.68   0.094    -.5351943    .0428858
     comform |   .2905017   .3257971     0.89   0.374    -.3533134    .9343168
         dom |   .0517454   .1456518     0.36   0.723    -.2360803    .3395711
    econfood |   .0164922   .0384115     0.43   0.668    -.0594135     .092398
       house |   .1097122   .1206444     0.91   0.365    -.1286958    .3481203
      llomue |  -.0286578   .2258123    -0.13   0.899    -.4748905    .4175749
     chitsua |   .5726932   .4495858     1.27   0.205    -.3157433     1.46113
      living |   .0025424   .0471087     0.05   0.957    -.0905501    .0956349
       _cons |  -.2024256   .2537087    -0.80   0.426     -.703785    .2989339
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     424
-------------+------------------------------           F( 25,   398) =    0.99
       Model |  21.3329691    25  .853318763           Prob > F      =  0.4860
    Residual |  344.702933   398  .866087771           R-squared     =  0.0583
-------------+------------------------------           Adj R-squared = -0.0009
       Total |  366.035902   423  .865333102           Root MSE      =  .93064

------------------------------------------------------------------------------
zscvcou~2009 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.109257   .1493807    -0.73   0.465    -.4029308    .1844169
     hotline |   .0268138   .1398156     0.19   0.848    -.2480557    .3016832
     verdade |  -.1973577   .1470386    -1.34   0.180    -.4864272    .0917118
         pr1 |   .3288488   .1591465     2.07   0.039     .0159758    .6417217
         pr2 |   .1538936   .1528596     1.01   0.315    -.1466196    .4544068
         pr3 |   .2498482   .1492914     1.67   0.095      -.04365    .5433464
        post |   .0459026   .1381096     0.33   0.740    -.2256129     .317418
   post_miss |   .2089701   .2297153     0.91   0.364    -.2426368    .6605771
      health |   .1224604   .1144465     1.07   0.285    -.1025348    .3474556
 health_miss |  -.2014043   .3260118    -0.62   0.537    -.8423248    .4395162
         sex |   .0973254   .0975065     1.00   0.319    -.0943667    .2890176
         age |  -.0065395   .0039698    -1.65   0.100    -.0143438    .0012649
      single |  -.1653535   .1198007    -1.38   0.168    -.4008747    .0701677
       divor |   .4590567   .5486734     0.84   0.403    -.6196034    1.537717
     protest |   .0754461   .1164299     0.65   0.517    -.1534483    .3043406
         com |   .0111951   .2421389     0.05   0.963     -.464836    .4872263
        prof |   -.304507   .3412293    -0.89   0.373     -.975344    .3663301
         tea |  -.2461543   .2002875    -1.23   0.220    -.6399079    .1475993
     comform |   .2905017   .3915266     0.74   0.459     -.479217     1.06022
         dom |   .0517454   .1443892     0.36   0.720    -.2321155    .3356062
    econfood |   .0164922   .0425373     0.39   0.698    -.0671336    .1001181
       house |   .1097122   .1363693     0.80   0.422     -.158382    .3778065
      llomue |  -.0286578   .1890431    -0.15   0.880    -.4003056      .34299
     chitsua |   .5726932   .4878845     1.17   0.241    -.3864597    1.531846
      living |   .0025424   .0445524     0.06   0.955     -.085045    .0901298
       _cons |  -.2024256   .2477703    -0.82   0.414    -.6895278    .2846766
------------------------------------------------------------------------------

Simultaneous results for zscvcount2009_3_2a, zscvcount2009_3_3a

                                                  Number of obs   =       1088

                                               (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------------------
                         |               Robust
                         |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
zscvcount2009_3_2a_mean  |
               civiceduc |   -.145953   .0849526    -1.72   0.086    -.3124571    .0205511
                 hotline |  -.0493528   .0897497    -0.55   0.582     -.225259    .1265535
                 verdade |  -.0554302   .0912149    -0.61   0.543    -.2342081    .1233477
                     pr1 |   .3051494   .1119355     2.73   0.006     .0857598    .5245389
                     pr2 |   .0901483   .1007133     0.90   0.371    -.1072462    .2875427
                     pr3 |   .1165014   .0953141     1.22   0.222    -.0703109    .3033137
                    post |  -.0727173    .107779    -0.67   0.500    -.2839603    .1385256
               post_miss |  -.0013967   .1355592    -0.01   0.992    -.2670878    .2642943
                  health |   .0291521   .0672919     0.43   0.665    -.1027376    .1610418
             health_miss |  -.2553802   .1064286    -2.40   0.016    -.4639764   -.0467839
                     sex |   .0203855   .0587888     0.35   0.729    -.0948385    .1356095
                     age |  -.0030719   .0029872    -1.03   0.304    -.0089267    .0027829
                  single |    .031687   .0941624     0.34   0.736    -.1528679     .216242
                   divor |  -.2389447   .1767696    -1.35   0.176    -.5854068    .1075173
                 protest |   .0372174   .0770992     0.48   0.629    -.1138943    .1883291
                     com |  -.2196827   .1011543    -2.17   0.030    -.4179416   -.0214238
                    prof |   .3142541   .3536394     0.89   0.374    -.3788664    1.007375
                     tea |  -.0620103   .1399995    -0.44   0.658    -.3364043    .2123837
                 comform |   .1884057    .319782     0.59   0.556    -.4383555     .815167
                     dom |  -.1509297   .0848749    -1.78   0.075    -.3172815     .015422
                econfood |   .0376589   .0279514     1.35   0.178    -.0171249    .0924427
                   house |   .1305038   .0767642     1.70   0.089    -.0199513    .2809588
                  llomue |  -.0499059   .1636534    -0.30   0.760    -.3706606    .2708489
                 chitsua |   .2021868   .3052301     0.66   0.508    -.3960532    .8004268
                  living |   -.009863   .0322737    -0.31   0.760    -.0731184    .0533923
                   _cons |   -.129219   .1499013    -0.86   0.389      -.42302    .1645821
-------------------------+----------------------------------------------------------------
zscvcount2009_3_2a_lnvar |
                   _cons |  -.0560124    .091698    -0.61   0.541    -.2357371    .1237124
-------------------------+----------------------------------------------------------------
zscvcount2009_3_3a_mean  |
               civiceduc |   -.109257    .142733    -0.77   0.444    -.3890085    .1704946
                 hotline |   .0268138   .1155271     0.23   0.816    -.1996151    .2532427
                 verdade |  -.1973577   .1445902    -1.36   0.172    -.4807493    .0860339
                     pr1 |   .3288488   .1713443     1.92   0.055    -.0069799    .6646775
                     pr2 |   .1538936   .1683926     0.91   0.361    -.1761497     .483937
                     pr3 |   .2498482   .1273707     1.96   0.050     .0002063    .4994901
                    post |   .0459026   .1491134     0.31   0.758    -.2463544    .3381596
               post_miss |   .2089701   .0853976     2.45   0.014     .0415939    .3763463
                  health |   .1224604   .1013955     1.21   0.227    -.0762711    .3211918
             health_miss |  -.2014043   .1501106    -1.34   0.180    -.4956157    .0928071
                     sex |   .0973254   .0958106     1.02   0.310    -.0904599    .2851107
                     age |  -.0065395   .0047165    -1.39   0.166    -.0157836    .0027046
                  single |  -.1653535   .1027872    -1.61   0.108    -.3668127    .0361057
                   divor |   .4590567   .7213237     0.64   0.525    -.9547118    1.872825
                 protest |   .0754461   .1256771     0.60   0.548    -.1708765    .3217687
                     com |   .0111951   .2098371     0.05   0.957    -.4000779    .4224682
                    prof |   -.304507   .2547296    -1.20   0.232    -.8037678    .1947539
                     tea |  -.2461543   .1418425    -1.74   0.083    -.5241604    .0318518
                 comform |   .2905017   .3159434     0.92   0.358     -.328736    .9097394
                     dom |   .0517454   .1412465     0.37   0.714    -.2250927    .3285834
                econfood |   .0164922   .0372497     0.44   0.658    -.0565158    .0895003
                   house |   .1097122   .1169955     0.94   0.348    -.1195947    .3390192
                  llomue |  -.0286578   .2189826    -0.13   0.896    -.4578558    .4005401
                 chitsua |   .5726932    .435988     1.31   0.189    -.2818277    1.427214
                  living |   .0025424   .0456839     0.06   0.956    -.0869963    .0920812
                   _cons |  -.2024256   .2460353    -0.82   0.411    -.6846458    .2797947
-------------------------+----------------------------------------------------------------
zscvcount2009_3_3a_lnvar |
                   _cons |   -.143769   .1303551    -1.10   0.270    -.3992603    .1117222
------------------------------------------------------------------------------------------

 ( 1)  [zscvcount2009_3_2a_mean]civiceduc - [zscvcount2009_3_3a_mean]civiceduc = 0

           chi2(  1) =    0.07
         Prob > chi2 =    0.7987
.79870702

 ( 1)  [zscvcount2009_3_2a_mean]hotline - [zscvcount2009_3_3a_mean]hotline = 0

           chi2(  1) =    0.41
         Prob > chi2 =    0.5201
.52005207

 ( 1)  [zscvcount2009_3_2a_mean]verdade - [zscvcount2009_3_3a_mean]verdade = 0

           chi2(  1) =    0.93
         Prob > chi2 =    0.3337
.33368859

. 
. matrix define means=(m_zscvcount2009_2_1, m_zscvcount2009_3_1 \ t_zscvcount2009_2_1_1, t_zscvc
> ount2009_3_1_1 \ t_zscvcount2009_2_1_2, t_zscvcount2009_3_1_2 \ t_zscvcount2009_2_1_3, t_zscvc
> ount2009_3_1_3 \ t_zscvcount2009_2_1_4, t_zscvcount2009_3_1_4 \ t_zscvcount2009_2_5, t_zscvcou
> nt2009_3_5 \ t_zscvcount2009_2_6, t_zscvcount2009_3_6 \ t_zscvcount2009_2_7, t_zscvcount2009_3
> _7)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_survey.xml") append sheet("vcount
> 2009") 


note: results saved to outputregs_survey.xml

. xml_tab $list2, save("outputregs_survey.xml") append sheet("vcount2009 stats") 


note: results saved to outputregs_survey.xml

. estimates clear

. 
. *votbuying
. 
. global final="zzscvotbuying"

. 
. global list1=""

. global list2=""

. 
. foreach i in $final {
  2. 
.         regress `i' $treat $prov if time==1, cluster(ea)
  3.         estimates store `i'_2_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2_1=r(mean)
  6.         display m_`i'_2_1
  7.         test civiceduc = hotline
  8.         scalar define t_`i'_2_1_1=r(p)
  9.         display t_`i'_2_1_1
 10.         test civiceduc = verdade
 11.         scalar define t_`i'_2_1_2=r(p)
 12.         display t_`i'_2_1_2
 13.         test hotline = verdade
 14.         scalar define t_`i'_2_1_3=r(p)
 15.         display t_`i'_2_1_3
 16.         test civiceduc hotline verdade
 17.         scalar define t_`i'_2_1_4=r(p)
 18.         display t_`i'_2_1_4
 19. 
.         regress `i' $treat $prov if time==1 & lazy==0, cluster(ea)
 20.         estimates store `i'_2_2
 21.         regress `i' $treat $prov if time==1 & lazy==0
 22.         estimates store `i'_2_2a
 23.         
.         regress `i' $treat $prov if time==1 & (lazy==1|control==1), cluster(ea)
 24.         estimates store `i'_2_3
 25.         regress `i' $treat $prov if time==1 & (lazy==1|control==1)
 26.         estimates store `i'_2_3a
 27. 
.         suest `i'_2_2a `i'_2_3a, cluster(ea)
 28.         test [`i'_2_2a_mean]civiceduc=[`i'_2_3a_mean]civiceduc  
 29.         scalar define t_`i'_2_5=r(p)
 30.         display t_`i'_2_5
 31.         test [`i'_2_2a_mean]hotline=[`i'_2_3a_mean]hotline      
 32.         scalar define t_`i'_2_6=r(p)
 33.         display t_`i'_2_6
 34.         test [`i'_2_2a_mean]verdade=[`i'_2_3a_mean]verdade
 35.         scalar define t_`i'_2_7=r(p)
 36.         display t_`i'_2_7
 37. 
.         regress `i' $treat $prov $ea $controls if time==1, cluster(ea)
 38.         estimates store `i'_3_1
 39.         sum `i' if e(sample) & control == 1
 40.         scalar define m_`i'_3_1=r(mean)
 41.         display m_`i'_3_1
 42.         test civiceduc = hotline
 43.         scalar define t_`i'_3_1_1=r(p)
 44.         display t_`i'_3_1_1
 45.         test civiceduc = verdade
 46.         scalar define t_`i'_3_1_2=r(p)
 47.         display t_`i'_3_1_2
 48.         test hotline = verdade
 49.         scalar define t_`i'_3_1_3=r(p)
 50.         display t_`i'_3_1_3
 51.         test civiceduc hotline verdade
 52.         scalar define t_`i'_3_1_4=r(p)
 53.         display t_`i'_3_1_4
 54. 
.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0, cluster(ea)
 55.         estimates store `i'_3_2
 56.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0
 57.         estimates store `i'_3_2a
 58.         
.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1), cluster(ea)
 59.         estimates store `i'_3_3
 60.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1)
 61.         estimates store `i'_3_3a
 62. 
.         suest `i'_3_2a `i'_3_3a, cluster(ea)
 63.         test [`i'_3_2a_mean]civiceduc=[`i'_3_3a_mean]civiceduc  
 64.         scalar define t_`i'_3_5=r(p)
 65.         display t_`i'_3_5
 66.         test [`i'_3_2a_mean]hotline=[`i'_3_3a_mean]hotline      
 67.         scalar define t_`i'_3_6=r(p)
 68.         display t_`i'_3_6
 69.         test [`i'_3_2a_mean]verdade=[`i'_3_3a_mean]verdade
 70.         scalar define t_`i'_3_7=r(p)
 71.         display t_`i'_3_7
 72.         
.         global list1="$list1" + " `i'_2_1" + " `i'_3_1" + " `i'_2_2" + " `i'_3_2"  + " `i'_2_3
> " + " `i'_3_3"
 73.         
.         }

Linear regression                                      Number of obs =    1131
                                                       F(  6,   160) =    1.61
                                                       Prob > F      =  0.1465
                                                       R-squared     =  0.0133
                                                       Root MSE      =  .73372

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zzscvotbuy~g |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0385668   .0615473    -0.63   0.532    -.1601167    .0829831
     hotline |   .0483507   .0634156     0.76   0.447    -.0768889    .1735904
     verdade |   .1163366   .0665642     1.75   0.082    -.0151212    .2477943
         pr1 |   .1455765   .0738393     1.97   0.050    -.0002488    .2914018
         pr2 |  -.0042956   .0611853    -0.07   0.944    -.1251306    .1165393
         pr3 |   .0234706   .0587906     0.40   0.690    -.0926349    .1395762
       _cons |   -.042794   .0620465    -0.69   0.491    -.1653299    .0797418
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zzscvotbuy~g |       271    1.54e-08     .699602  -.6641259   3.749353
1.537e-08

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    1.87
            Prob > F =    0.1729
.17293047

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    5.35
            Prob > F =    0.0221
.02205376

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.98
            Prob > F =    0.3238
.32381874

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    1.99
            Prob > F =    0.1183
.11828642

Linear regression                                      Number of obs =     960
                                                       F(  6,   160) =    1.82
                                                       Prob > F      =  0.0977
                                                       R-squared     =  0.0148
                                                       Root MSE      =  .74134

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zzscvotbuy~g |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0220904   .0630371    -0.35   0.726    -.1465824    .1024017
     hotline |   .0308763   .0665699     0.46   0.643    -.1005926    .1623453
     verdade |   .1641039   .0703199     2.33   0.021      .025229    .3029788
         pr1 |   .1138272   .0750596     1.52   0.131    -.0344081    .2620625
         pr2 |  -.0149698   .0683895    -0.22   0.827    -.1500324    .1200928
         pr3 |  -.0147786   .0659931    -0.22   0.823    -.1451086    .1155513
       _cons |  -.0225216   .0641678    -0.35   0.726    -.1492468    .1042036
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     960
-------------+------------------------------           F(  6,   953) =    2.38
       Model |  7.86168399     6  1.31028067           Prob > F      =  0.0272
    Residual |  523.752486   953  .549582881           R-squared     =  0.0148
-------------+------------------------------           Adj R-squared =  0.0086
       Total |   531.61417   959    .5543422           Root MSE      =  .74134

------------------------------------------------------------------------------
zzscvotbuy~g |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0220904   .0655019    -0.34   0.736     -.150635    .1064542
     hotline |   .0308763   .0666561     0.46   0.643    -.0999334     .161686
     verdade |   .1641039   .0674782     2.43   0.015      .031681    .2965269
         pr1 |   .1138272   .0669738     1.70   0.090     -.017606    .2452604
         pr2 |  -.0149698   .0677542    -0.22   0.825    -.1479345    .1179948
         pr3 |  -.0147786   .0679954    -0.22   0.828    -.1482166    .1186594
       _cons |  -.0225216   .0611928    -0.37   0.713    -.1426098    .0975666
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     442
                                                       F(  6,   151) =    0.64
                                                       Prob > F      =  0.6997
                                                       R-squared     =  0.0088
                                                       Root MSE      =  .69719

                                   (Std. Err. adjusted for 152 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zzscvotbuy~g |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1123422   .1178891    -0.95   0.342    -.3452673    .1205829
     hotline |   .1096583   .0981479     1.12   0.266    -.0842623    .3035789
     verdade |  -.0751608   .1085192    -0.69   0.490    -.2895729    .1392514
         pr1 |   .0314842   .1045891     0.30   0.764    -.1751629    .2381313
         pr2 |  -.0153707   .0947799    -0.16   0.871    -.2026367    .1718953
         pr3 |  -.0136684   .0923087    -0.15   0.882    -.1960519     .168715
       _cons |  -.0011196    .079654    -0.01   0.989       -.1585    .1562607
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     442
-------------+------------------------------           F(  6,   435) =    0.64
       Model |  1.87169222     6  .311948703           Prob > F      =  0.6968
    Residual |  211.444112   435  .486078419           R-squared     =  0.0088
-------------+------------------------------           Adj R-squared = -0.0049
       Total |  213.315805   441  .483709308           Root MSE      =  .69719

------------------------------------------------------------------------------
zzscvotbuy~g |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1123422   .1040143    -1.08   0.281    -.3167752    .0920908
     hotline |   .1096583    .099084     1.11   0.269    -.0850847    .3044012
     verdade |  -.0751608   .1024823    -0.73   0.464    -.2765828    .1262613
         pr1 |   .0314842   .0924549     0.34   0.734    -.1502297    .2131981
         pr2 |  -.0153707   .0945936    -0.16   0.871     -.201288    .1705466
         pr3 |  -.0136684   .0927089    -0.15   0.883    -.1958815    .1685446
       _cons |  -.0011196   .0708713    -0.02   0.987    -.1404124    .1381732
------------------------------------------------------------------------------

Simultaneous results for zzscvotbuying_2_2a, zzscvotbuying_2_3a

                                                  Number of obs   =       1131

                                               (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------------------
                         |               Robust
                         |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
zzscvotbuying_2_2a_mean  |
               civiceduc |  -.0220904   .0628396    -0.35   0.725    -.1452537     .101073
                 hotline |   .0308763   .0663613     0.47   0.642    -.0991895    .1609421
                 verdade |   .1641039   .0700996     2.34   0.019     .0267113    .3014966
                     pr1 |   .1138272   .0748244     1.52   0.128     -.032826    .2604804
                     pr2 |  -.0149698   .0681753    -0.22   0.826    -.1485909    .1186513
                     pr3 |  -.0147786   .0657864    -0.22   0.822    -.1437176    .1141603
                   _cons |  -.0225216   .0639668    -0.35   0.725    -.1478942     .102851
-------------------------+----------------------------------------------------------------
zzscvotbuying_2_2a_lnvar |
                   _cons |  -.5985957    .072216    -8.29   0.000    -.7401365   -.4570549
-------------------------+----------------------------------------------------------------
zzscvotbuying_2_3a_mean  |
               civiceduc |  -.1123422   .1170627    -0.96   0.337    -.3417809    .1170965
                 hotline |   .1096583   .0974599     1.13   0.261    -.0813597    .3006762
                 verdade |  -.0751608   .1077585    -0.70   0.485    -.2863635     .136042
                     pr1 |   .0314842    .103856     0.30   0.762    -.1720698    .2350382
                     pr2 |  -.0153707   .0941155    -0.16   0.870    -.1998337    .1690923
                     pr3 |  -.0136684   .0916616    -0.15   0.881    -.1933219    .1659851
                   _cons |  -.0011196   .0790957    -0.01   0.989    -.1561443     .153905
-------------------------+----------------------------------------------------------------
zzscvotbuying_2_3a_lnvar |
                   _cons |  -.7213853   .1266608    -5.70   0.000    -.9696358   -.4731348
------------------------------------------------------------------------------------------

 ( 1)  [zzscvotbuying_2_2a_mean]civiceduc - [zzscvotbuying_2_3a_mean]civiceduc = 0

           chi2(  1) =    0.61
         Prob > chi2 =    0.4344
.43439174

 ( 1)  [zzscvotbuying_2_2a_mean]hotline - [zzscvotbuying_2_3a_mean]hotline = 0

           chi2(  1) =    0.65
         Prob > chi2 =    0.4185
.418524

 ( 1)  [zzscvotbuying_2_2a_mean]verdade - [zzscvotbuying_2_3a_mean]verdade = 0

           chi2(  1) =    4.86
         Prob > chi2 =    0.0274
.02741555

Linear regression                                      Number of obs =    1115
                                                       F( 25,   160) =    1.74
                                                       Prob > F      =  0.0221
                                                       R-squared     =  0.0375
                                                       Root MSE      =  .72952

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zzscvotbuy~g |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.020675   .0626964    -0.33   0.742    -.1444942    .1031442
     hotline |   .0385274   .0650498     0.59   0.555    -.0899396    .1669943
     verdade |   .1405504   .0679255     2.07   0.040     .0064042    .2746966
         pr1 |   .2568639   .0860184     2.99   0.003     .0869861    .4267417
         pr2 |   .1174309   .0676906     1.73   0.085    -.0162515    .2511132
         pr3 |   .0686289   .0681341     1.01   0.315    -.0659292    .2031869
        post |  -.0167432   .0738192    -0.23   0.821    -.1625289    .1290425
   post_miss |   .1689177   .0930798     1.81   0.071    -.0149058    .3527411
      health |   .0857757   .0544846     1.57   0.117    -.0218261    .1933775
 health_miss |  -.2031707   .1512937    -1.34   0.181     -.501961    .0956195
         sex |   .0066476   .0435337     0.15   0.879    -.0793273    .0926224
         age |  -.0000209   .0019035    -0.01   0.991    -.0037801    .0037384
      single |   .0691155    .054883     1.26   0.210     -.039273    .1775041
       divor |   .0997978   .2351212     0.42   0.672    -.3645434    .5641391
     protest |  -.1179034   .0441625    -2.67   0.008    -.2051199   -.0306869
         com |  -.1261998   .1045156    -1.21   0.229    -.3326078    .0802082
        prof |   .1222184   .1642292     0.74   0.458    -.2021181    .4465549
         tea |   -.068075   .0812444    -0.84   0.403    -.2285246    .0923746
     comform |    .080337   .1776289     0.45   0.652    -.2704627    .4311366
         dom |  -.0970859   .0562757    -1.73   0.086    -.2082248     .014053
    econfood |   .0339564   .0190223     1.79   0.076    -.0036107    .0715235
       house |   .0336311    .068735     0.49   0.625    -.1021137     .169376
      llomue |  -.1196593   .0950543    -1.26   0.210    -.3073822    .0680637
     chitsua |  -.1897362   .1587226    -1.20   0.234    -.5031978    .1237253
      living |   .0280417   .0202144     1.39   0.167    -.0118798    .0679632
       _cons |  -.2655001   .1318208    -2.01   0.046    -.5258332    -.005167
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zzscvotbuy~g |       268    .0028374    .7015495  -.6641259   3.749353
.00283738

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    0.81
            Prob > F =    0.3705
.37045588

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    6.04
            Prob > F =    0.0150
.01501642

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    2.16
            Prob > F =    0.1439
.14388934

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    2.25
            Prob > F =    0.0844
.08438364

Linear regression                                      Number of obs =     949
                                                       F( 25,   160) =    2.07
                                                       Prob > F      =  0.0037
                                                       R-squared     =  0.0409
                                                       Root MSE      =  .74154

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zzscvotbuy~g |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0086956    .062793    -0.14   0.890    -.1327055    .1153143
     hotline |   .0363112    .068437     0.53   0.596    -.0988452    .1714676
     verdade |   .1892635   .0730041     2.59   0.010     .0450875    .3334395
         pr1 |   .2192987   .0842345     2.60   0.010     .0529438    .3856536
         pr2 |   .1099646   .0756756     1.45   0.148    -.0394873    .2594164
         pr3 |   .0290888   .0745762     0.39   0.697    -.1181919    .1763696
        post |  -.0050506   .0855687    -0.06   0.953    -.1740404    .1639392
   post_miss |   .1661428   .0880855     1.89   0.061    -.0078173    .3401029
      health |   .0463204   .0565504     0.82   0.414    -.0653611     .158002
 health_miss |  -.2388396   .1485693    -1.61   0.110    -.5322493    .0545702
         sex |   .0235237   .0490084     0.48   0.632    -.0732629    .1203104
         age |   .0000687   .0020161     0.03   0.973    -.0039129    .0040502
      single |   .1023204   .0637915     1.60   0.111    -.0236615    .2283022
       divor |  -.0593761   .1586877    -0.37   0.709    -.3727687    .2540165
     protest |  -.1240555   .0496884    -2.50   0.014    -.2221853   -.0259257
         com |  -.1040087   .1097985    -0.95   0.345    -.3208499    .1128326
        prof |   .2251307    .165062     1.36   0.175    -.1008505    .5511119
         tea |  -.0988387   .0947207    -1.04   0.298    -.2859027    .0882254
     comform |    .242633   .1881002     1.29   0.199    -.1288463    .6141123
         dom |  -.1037198    .064682    -1.60   0.111    -.2314604    .0240209
    econfood |   .0342156   .0206447     1.66   0.099    -.0065557    .0749869
       house |    .028346   .0792263     0.36   0.721     -.128118    .1848101
      llomue |  -.1432166    .102473    -1.40   0.164    -.3455906    .0591575
     chitsua |  -.2464261   .1953648    -1.26   0.209    -.6322524    .1394001
      living |   .0227672   .0221645     1.03   0.306    -.0210054    .0665399
       _cons |  -.2087667   .1413506    -1.48   0.142    -.4879202    .0703869
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     949
-------------+------------------------------           F( 25,   923) =    1.57
       Model |  21.6479896    25  .865919583           Prob > F      =  0.0366
    Residual |   507.53468   923  .549875059           R-squared     =  0.0409
-------------+------------------------------           Adj R-squared =  0.0149
       Total |  529.182669   948  .558209567           Root MSE      =  .74154

------------------------------------------------------------------------------
zzscvotbuy~g |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0086956   .0691017    -0.13   0.900    -.1443103    .1269191
     hotline |   .0363112   .0688619     0.53   0.598    -.0988329    .1714553
     verdade |   .1892635   .0702288     2.69   0.007     .0514369    .3270902
         pr1 |   .2192987   .0824812     2.66   0.008     .0574262    .3811712
         pr2 |   .1099646   .0811953     1.35   0.176    -.0493843    .2693134
         pr3 |   .0290888   .0787589     0.37   0.712    -.1254784    .1836561
        post |  -.0050506   .0841374    -0.06   0.952    -.1701734    .1600722
   post_miss |   .1661428   .1388895     1.20   0.232    -.1064331    .4387187
      health |   .0463204   .0577701     0.80   0.423    -.0670556    .1596965
 health_miss |  -.2388396   .1566417    -1.52   0.128    -.5462548    .0685757
         sex |   .0235237   .0517849     0.45   0.650    -.0781061    .1251536
         age |   .0000687   .0020067     0.03   0.973    -.0038696    .0040069
      single |   .1023204   .0669369     1.53   0.127    -.0290459    .2336866
       divor |  -.0593761   .2860548    -0.21   0.836    -.6207694    .5020171
     protest |  -.1240555   .0594381    -2.09   0.037     -.240705    -.007406
         com |  -.1040087   .1142734    -0.91   0.363    -.3282746    .1202572
        prof |   .2251307   .1960326     1.15   0.251    -.1595907    .6098521
         tea |  -.0988387   .1204062    -0.82   0.412    -.3351402    .1374629
     comform |    .242633   .2399758     1.01   0.312    -.2283286    .7135946
         dom |  -.1037198   .0757033    -1.37   0.171    -.2522902    .0448507
    econfood |   .0342156   .0214778     1.59   0.111    -.0079354    .0763665
       house |    .028346   .0716329     0.40   0.692    -.1122362    .1689282
      llomue |  -.1432166    .097183    -1.47   0.141    -.3339418    .0475087
     chitsua |  -.2464261    .241136    -1.02   0.307    -.7196645    .2268123
      living |   .0227672   .0243075     0.94   0.349    -.0249371    .0704715
       _cons |  -.2087667   .1334581    -1.56   0.118    -.4706832    .0531499
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     434
                                                       F( 25,   150) =    1.13
                                                       Prob > F      =  0.3176
                                                       R-squared     =  0.0456
                                                       Root MSE      =  .69008

                                   (Std. Err. adjusted for 151 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zzscvotbuy~g |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0715407   .1233483    -0.58   0.563    -.3152653     .172184
     hotline |   .0482583   .0931789     0.52   0.605    -.1358543    .2323709
     verdade |  -.0474151   .1145424    -0.41   0.680      -.27374    .1789099
         pr1 |   .1286857   .1296882     0.99   0.323    -.1275658    .3849372
         pr2 |   .0872509   .1011481     0.86   0.390    -.1126081    .2871098
         pr3 |   .0281021    .092912     0.30   0.763    -.1554832    .2116873
        post |  -.0521034   .0989376    -0.53   0.599    -.2475947     .143388
   post_miss |   .1443444   .1160372     1.24   0.215    -.0849341    .3736229
      health |   .2229943   .0792808     2.81   0.006     .0663429    .3796457
 health_miss |   .0053032   .2831143     0.02   0.985    -.5541039    .5647103
         sex |   .0222455   .0665552     0.33   0.739    -.1092612    .1537522
         age |  -.0010025   .0028044    -0.36   0.721    -.0065437    .0045388
      single |   .0099123   .0784792     0.13   0.900    -.1451551    .1649798
       divor |   .4990044   .5156627     0.97   0.335    -.5198963    1.517905
     protest |  -.0917711   .0705896    -1.30   0.196    -.2312495    .0477073
         com |  -.2190844    .158297    -1.38   0.168    -.5318643    .0936955
        prof |  -.0709766   .2600004    -0.27   0.785    -.5847127    .4427595
         tea |  -.0199467   .1278087    -0.16   0.876    -.2724845    .2325911
     comform |  -.0731681   .1399895    -0.52   0.602    -.3497741    .2034379
         dom |  -.0757514   .0988364    -0.77   0.445    -.2710428      .11954
    econfood |   .0256556   .0281015     0.91   0.363    -.0298704    .0811815
       house |  -.0104832    .075986    -0.14   0.890    -.1606242    .1396579
      llomue |  -.0339354   .1258481    -0.27   0.788    -.2825994    .2147285
     chitsua |   .0427622   .1751627     0.24   0.807    -.3033428    .3888672
      living |   .0323294   .0313343     1.03   0.304    -.0295842    .0942431
       _cons |   -.260327   .1622313    -1.60   0.111    -.5808808    .0602268
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     434
-------------+------------------------------           F( 25,   408) =    0.78
       Model |  9.28213893    25  .371285557           Prob > F      =  0.7689
    Residual |  194.292542   408  .476207212           R-squared     =  0.0456
-------------+------------------------------           Adj R-squared = -0.0129
       Total |  203.574681   433   .47014938           Root MSE      =  .69008

------------------------------------------------------------------------------
zzscvotbuy~g |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0715407   .1087438    -0.66   0.511    -.2853087    .1422273
     hotline |   .0482583    .103407     0.47   0.641    -.1550187    .2515353
     verdade |  -.0474151     .10803    -0.44   0.661    -.2597799    .1649498
         pr1 |   .1286857   .1170412     1.10   0.272    -.1013934    .3587648
         pr2 |   .0872509   .1126895     0.77   0.439    -.1342737    .3087754
         pr3 |   .0281021   .1101927     0.26   0.799    -.1885142    .2447183
        post |  -.0521034   .1002519    -0.52   0.604    -.2491782    .1449714
   post_miss |   .1443444   .1635605     0.88   0.378    -.1771821    .4658709
      health |   .2229943   .0846323     2.63   0.009     .0566245    .3893641
 health_miss |   .0053032   .2403319     0.02   0.982    -.4671401    .4777464
         sex |   .0222455   .0718814     0.31   0.757    -.1190586    .1635496
         age |  -.0010025   .0029188    -0.34   0.731    -.0067403    .0047353
      single |   .0099123   .0876351     0.11   0.910    -.1623603     .182185
       divor |   .4990044   .4064391     1.23   0.220    -.2999716    1.297981
     protest |  -.0917711   .0852502    -1.08   0.282    -.2593556    .0758133
         com |  -.2190844    .174379    -1.26   0.210    -.5618779     .123709
        prof |  -.0709766   .2383852    -0.30   0.766     -.539593    .3976398
         tea |  -.0199467   .1456862    -0.14   0.891     -.306336    .2664426
     comform |  -.0731681   .2901814    -0.25   0.801    -.6436053    .4972691
         dom |  -.0757514   .1046919    -0.72   0.470    -.2815542    .1300514
    econfood |   .0256556   .0311541     0.82   0.411     -.035587    .0868982
       house |  -.0104832   .0993471    -0.11   0.916    -.2057794     .184813
      llomue |  -.0339354   .1386545    -0.24   0.807    -.3065018     .238631
     chitsua |   .0427622   .3606765     0.12   0.906    -.6662541    .7517785
      living |   .0323294   .0326299     0.99   0.322    -.0318142     .096473
       _cons |   -.260327    .182167    -1.43   0.154      -.61843     .097776
------------------------------------------------------------------------------

Simultaneous results for zzscvotbuying_3_2a, zzscvotbuying_3_3a

                                                  Number of obs   =       1115

                                               (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------------------
                         |               Robust
                         |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
zzscvotbuying_3_2a_mean  |
               civiceduc |  -.0086956   .0619595    -0.14   0.888    -.1301339    .1127427
                 hotline |   .0363112   .0675286     0.54   0.591    -.0960425    .1686648
                 verdade |   .1892635   .0720351     2.63   0.009     .0480773    .3304497
                     pr1 |   .2192987   .0831164     2.64   0.008     .0563935    .3822039
                     pr2 |   .1099646   .0746711     1.47   0.141    -.0363881    .2563172
                     pr3 |   .0290888   .0735863     0.40   0.693    -.1151378    .1733154
                    post |  -.0050506   .0844329    -0.06   0.952     -.170536    .1604348
               post_miss |   .1661428   .0869162     1.91   0.056    -.0042099    .3364955
                  health |   .0463204   .0557998     0.83   0.406    -.0630452     .155686
             health_miss |  -.2388396   .1465972    -1.63   0.103    -.5261648    .0484857
                     sex |   .0235237   .0483578     0.49   0.627    -.0712559    .1183033
                     age |   .0000687   .0019893     0.03   0.972    -.0038303    .0039676
                  single |   .1023204   .0629447     1.63   0.104     -.021049    .2256897
                   divor |  -.0593761   .1565813    -0.38   0.705    -.3662699    .2475176
                 protest |  -.1240555   .0490289    -2.53   0.011    -.2201504   -.0279606
                     com |  -.1040087   .1083411    -0.96   0.337    -.3163533     .108336
                    prof |   .2251307    .162871     1.38   0.167    -.0940906     .544352
                     tea |  -.0988387   .0934634    -1.06   0.290    -.2820235    .0843462
                 comform |    .242633   .1856034     1.31   0.191    -.1211429    .6064089
                     dom |  -.1037198   .0638235    -1.63   0.104    -.2288114    .0213719
                econfood |   .0342156   .0203707     1.68   0.093    -.0057102    .0741414
                   house |    .028346   .0781746     0.36   0.717    -.1248734    .1815655
                  llomue |  -.1432166   .1011128    -1.42   0.157    -.3413939    .0549608
                 chitsua |  -.2464261   .1927716    -1.28   0.201    -.6242515    .1313992
                  living |   .0227672   .0218703     1.04   0.298    -.0200977    .0656322
                   _cons |  -.2087667   .1394744    -1.50   0.134    -.4821314    .0645981
-------------------------+----------------------------------------------------------------
zzscvotbuying_3_2a_lnvar |
                   _cons |  -.5980642   .0735287    -8.13   0.000    -.7421777   -.4539507
-------------------------+----------------------------------------------------------------
zzscvotbuying_3_3a_mean  |
               civiceduc |  -.0715407   .1197098    -0.60   0.550    -.3061675    .1630861
                 hotline |   .0482583   .0904302     0.53   0.594    -.1289817    .2254983
                 verdade |  -.0474151   .1111636    -0.43   0.670    -.2652917    .1704616
                     pr1 |   .1286857   .1258626     1.02   0.307    -.1180004    .3753718
                     pr2 |   .0872509   .0981643     0.89   0.374    -.1051477    .2796494
                     pr3 |   .0281021   .0901712     0.31   0.755    -.1486303    .2048344
                    post |  -.0521034   .0960191    -0.54   0.587    -.2402974    .1360906
               post_miss |   .1443444   .1126143     1.28   0.200    -.0763755    .3650643
                  health |   .2229943   .0769422     2.90   0.004     .0721905    .3737982
             health_miss |   .0053032   .2747629     0.02   0.985    -.5332222    .5438285
                     sex |   .0222455   .0645919     0.34   0.731    -.1043523    .1488433
                     age |  -.0010025   .0027217    -0.37   0.713    -.0063369    .0043319
                  single |   .0099123   .0761642     0.13   0.896    -.1393667    .1591914
                   divor |   .4990044   .5004515     1.00   0.319    -.4818624    1.479871
                 protest |  -.0917711   .0685073    -1.34   0.180     -.226043    .0425008
                     com |  -.2190844   .1536275    -1.43   0.154    -.5201888      .08202
                    prof |  -.0709766   .2523308    -0.28   0.778    -.5655358    .4235826
                     tea |  -.0199467   .1240385    -0.16   0.872    -.2630577    .2231643
                 comform |  -.0731681     .13586    -0.54   0.590    -.3394488    .1931127
                     dom |  -.0757514   .0959209    -0.79   0.430    -.2637529    .1122501
                econfood |   .0256556   .0272726     0.94   0.347    -.0277977    .0791089
                   house |  -.0104832   .0737445    -0.14   0.887    -.1550197    .1340533
                  llomue |  -.0339354   .1221358    -0.28   0.781    -.2733172    .2054463
                 chitsua |   .0427622   .1699957     0.25   0.801    -.2904233    .3759477
                  living |   .0323294     .03041     1.06   0.288    -.0272731     .091932
                   _cons |   -.260327   .1574458    -1.65   0.098    -.5689151     .048261
-------------------------+----------------------------------------------------------------
zzscvotbuying_3_3a_lnvar |
                   _cons |  -.7419022   .1255561    -5.91   0.000    -.9879877   -.4958167
------------------------------------------------------------------------------------------

 ( 1)  [zzscvotbuying_3_2a_mean]civiceduc - [zzscvotbuying_3_3a_mean]civiceduc = 0

           chi2(  1) =    0.28
         Prob > chi2 =    0.5961
.59607449

 ( 1)  [zzscvotbuying_3_2a_mean]hotline - [zzscvotbuying_3_3a_mean]hotline = 0

           chi2(  1) =    0.02
         Prob > chi2 =    0.8954
.89538337

 ( 1)  [zzscvotbuying_3_2a_mean]verdade - [zzscvotbuying_3_3a_mean]verdade = 0

           chi2(  1) =    4.17
         Prob > chi2 =    0.0410
.04103589

. 
. matrix define means=(m_zzscvotbuying_2_1, m_zzscvotbuying_3_1 \ t_zzscvotbuying_2_1_1, t_zzscv
> otbuying_3_1_1 \ t_zzscvotbuying_2_1_2, t_zzscvotbuying_3_1_2 \ t_zzscvotbuying_2_1_3, t_zzscv
> otbuying_3_1_3 \ t_zzscvotbuying_2_1_4, t_zzscvotbuying_3_1_4 \ t_zzscvotbuying_2_5, t_zzscvot
> buying_3_5 \ t_zzscvotbuying_2_6, t_zzscvotbuying_3_6 \ t_zzscvotbuying_2_7, t_zzscvotbuying_3
> _7)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_survey.xml") append sheet("votbuy
> ing") 


note: results saved to outputregs_survey.xml

. xml_tab $list2, save("outputregs_survey.xml") append sheet("votbuying stats") 


note: results saved to outputregs_survey.xml

. estimates clear

. 
. *violence
. 
. global final="zzscviolence"

. 
. global list1=""

. global list2=""

. 
. foreach i in $final {
  2. 
.         regress `i' $treat $prov if time==1, cluster(ea)
  3.         estimates store `i'_2_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_2_1=r(mean)
  6.         display m_`i'_2_1
  7.         test civiceduc = hotline
  8.         scalar define t_`i'_2_1_1=r(p)
  9.         display t_`i'_2_1_1
 10.         test civiceduc = verdade
 11.         scalar define t_`i'_2_1_2=r(p)
 12.         display t_`i'_2_1_2
 13.         test hotline = verdade
 14.         scalar define t_`i'_2_1_3=r(p)
 15.         display t_`i'_2_1_3
 16.         test civiceduc hotline verdade
 17.         scalar define t_`i'_2_1_4=r(p)
 18.         display t_`i'_2_1_4
 19. 
.         regress `i' $treat $prov if time==1 & lazy==0, cluster(ea)
 20.         estimates store `i'_2_2
 21.         regress `i' $treat $prov if time==1 & lazy==0
 22.         estimates store `i'_2_2a
 23.         
.         regress `i' $treat $prov if time==1 & (lazy==1|control==1), cluster(ea)
 24.         estimates store `i'_2_3
 25.         regress `i' $treat $prov if time==1 & (lazy==1|control==1)
 26.         estimates store `i'_2_3a
 27. 
.         suest `i'_2_2a `i'_2_3a, cluster(ea)
 28.         test [`i'_2_2a_mean]civiceduc=[`i'_2_3a_mean]civiceduc  
 29.         scalar define t_`i'_2_5=r(p)
 30.         display t_`i'_2_5
 31.         test [`i'_2_2a_mean]hotline=[`i'_2_3a_mean]hotline      
 32.         scalar define t_`i'_2_6=r(p)
 33.         display t_`i'_2_6
 34.         test [`i'_2_2a_mean]verdade=[`i'_2_3a_mean]verdade
 35.         scalar define t_`i'_2_7=r(p)
 36.         display t_`i'_2_7
 37. 
.         regress `i' $treat $prov $ea $controls if time==1, cluster(ea)
 38.         estimates store `i'_3_1
 39.         sum `i' if e(sample) & control == 1
 40.         scalar define m_`i'_3_1=r(mean)
 41.         display m_`i'_3_1
 42.         test civiceduc = hotline
 43.         scalar define t_`i'_3_1_1=r(p)
 44.         display t_`i'_3_1_1
 45.         test civiceduc = verdade
 46.         scalar define t_`i'_3_1_2=r(p)
 47.         display t_`i'_3_1_2
 48.         test hotline = verdade
 49.         scalar define t_`i'_3_1_3=r(p)
 50.         display t_`i'_3_1_3
 51.         test civiceduc hotline verdade
 52.         scalar define t_`i'_3_1_4=r(p)
 53.         display t_`i'_3_1_4
 54. 
.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0, cluster(ea)
 55.         estimates store `i'_3_2
 56.         regress `i' $treat $prov $ea $controls if time==1 & lazy==0
 57.         estimates store `i'_3_2a
 58.         
.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1), cluster(ea)
 59.         estimates store `i'_3_3
 60.         regress `i' $treat $prov $ea $controls if time==1 & (lazy==1|control==1)
 61.         estimates store `i'_3_3a
 62. 
.         suest `i'_3_2a `i'_3_3a, cluster(ea)
 63.         test [`i'_3_2a_mean]civiceduc=[`i'_3_3a_mean]civiceduc  
 64.         scalar define t_`i'_3_5=r(p)
 65.         display t_`i'_3_5
 66.         test [`i'_3_2a_mean]hotline=[`i'_3_3a_mean]hotline      
 67.         scalar define t_`i'_3_6=r(p)
 68.         display t_`i'_3_6
 69.         test [`i'_3_2a_mean]verdade=[`i'_3_3a_mean]verdade
 70.         scalar define t_`i'_3_7=r(p)
 71.         display t_`i'_3_7
 72.         
.         global list1="$list1" + " `i'_2_1" + " `i'_3_1" + " `i'_2_2" + " `i'_3_2"  + " `i'_2_3
> " + " `i'_3_3"
 73.         
.         }

Linear regression                                      Number of obs =    1148
                                                       F(  6,   160) =    6.52
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0373
                                                       Root MSE      =  .45745

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zzscviolence |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1475235   .0407324    -3.62   0.000     -.227966   -.0670811
     hotline |  -.0581991   .0465814    -1.25   0.213    -.1501927    .0337946
     verdade |  -.0880142    .040056    -2.20   0.029    -.1671209   -.0089076
         pr1 |   .1975361   .0431876     4.57   0.000     .1122448    .2828275
         pr2 |   .1297845   .0328511     3.95   0.000     .0649068    .1946623
         pr3 |    .083791   .0387768     2.16   0.032     .0072106    .1603714
       _cons |  -.1039057   .0369883    -2.81   0.006     -.176954   -.0308575
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zzscviolence |       276    1.93e-09    .5017489  -.7026088   2.511753
1.934e-09

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    4.83
            Prob > F =    0.0295
.02946772

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    3.27
            Prob > F =    0.0723
.0723027

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.55
            Prob > F =    0.4601
.46012036

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    4.71
            Prob > F =    0.0035
.00352181

Linear regression                                      Number of obs =     973
                                                       F(  6,   160) =    5.39
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0363
                                                       Root MSE      =   .4555

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zzscviolence |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1401103   .0428019    -3.27   0.001    -.2246398   -.0555808
     hotline |  -.0554673   .0481862    -1.15   0.251    -.1506303    .0396957
     verdade |  -.0798241   .0412986    -1.93   0.055    -.1613847    .0017366
         pr1 |   .1932277   .0458264     4.22   0.000     .1027251    .2837303
         pr2 |   .1370822   .0366132     3.74   0.000     .0647748    .2093896
         pr3 |   .0863608   .0424141     2.04   0.043      .002597    .1701245
       _cons |   -.105255     .03763    -2.80   0.006    -.1795706   -.0309394
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     973
-------------+------------------------------           F(  6,   966) =    6.07
       Model |  7.55961546     6  1.25993591           Prob > F      =  0.0000
    Residual |   200.42357   966  .207477816           R-squared     =  0.0363
-------------+------------------------------           Adj R-squared =  0.0304
       Total |  207.983186   972  .213974471           Root MSE      =   .4555

------------------------------------------------------------------------------
zzscviolence |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1401103    .040028    -3.50   0.000    -.2186621   -.0615585
     hotline |  -.0554673   .0405428    -1.37   0.172    -.1350295    .0240949
     verdade |  -.0798241   .0411915    -1.94   0.053    -.1606591     .001011
         pr1 |   .1932277   .0410617     4.71   0.000     .1126474    .2738081
         pr2 |   .1370822   .0411979     3.33   0.001     .0562346    .2179299
         pr3 |   .0863608   .0416457     2.07   0.038     .0046342    .1680873
       _cons |   -.105255   .0373694    -2.82   0.005    -.1785895   -.0319205
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     451
                                                       F(  6,   152) =    3.05
                                                       Prob > F      =  0.0077
                                                       R-squared     =  0.0324
                                                       Root MSE      =  .49063

                                   (Std. Err. adjusted for 153 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zzscviolence |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1821614   .0576861    -3.16   0.002    -.2961315   -.0681913
     hotline |  -.0707434   .0900477    -0.79   0.433    -.2486501    .1071633
     verdade |  -.1194943   .0643845    -1.86   0.065    -.2466984    .0077098
         pr1 |   .1517879   .0768933     1.97   0.050    -.0001297    .3037055
         pr2 |   .1278663   .0573918     2.23   0.027     .0144778    .2412549
         pr3 |   .0911782   .0715689     1.27   0.205    -.0502201    .2325765
       _cons |  -.0934777    .047979    -1.95   0.053    -.1882695    .0013141
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     451
-------------+------------------------------           F(  6,   444) =    2.48
       Model |  3.58316332     6  .597193886           Prob > F      =  0.0227
    Residual |  106.879864   444  .240720415           R-squared     =  0.0324
-------------+------------------------------           Adj R-squared =  0.0194
       Total |  110.463028   450  .245473395           Root MSE      =  .49063

------------------------------------------------------------------------------
zzscviolence |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1821614   .0730822    -2.49   0.013    -.3257913   -.0385315
     hotline |  -.0707434   .0682001    -1.04   0.300    -.2047786    .0632918
     verdade |  -.1194943   .0714905    -1.67   0.095    -.2599962    .0210076
         pr1 |   .1517879   .0649178     2.34   0.020     .0242035    .2793723
         pr2 |   .1278663   .0655562     1.95   0.052    -.0009726    .2567053
         pr3 |   .0911782   .0646675     1.41   0.159    -.0359142    .2182706
       _cons |  -.0934777   .0495317    -1.89   0.060    -.1908234    .0038681
------------------------------------------------------------------------------

Simultaneous results for zzscviolence_2_2a, zzscviolence_2_3a

                                                  Number of obs   =       1148

                                              (Std. Err. adjusted for 161 clusters in ea)
-----------------------------------------------------------------------------------------
                        |               Robust
                        |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
zzscviolence_2_2a_mean  |
              civiceduc |  -.1401103   .0426696    -3.28   0.001    -.2237411   -.0564795
                hotline |  -.0554673   .0480373    -1.15   0.248    -.1496186     .038684
                verdade |  -.0798241   .0411709    -1.94   0.053    -.1605176    .0008695
                    pr1 |   .1932277   .0456847     4.23   0.000     .1036873    .2827681
                    pr2 |   .1370822      .0365     3.76   0.000     .0655436    .2086209
                    pr3 |   .0863608    .042283     2.04   0.041     .0034876     .169234
                  _cons |   -.105255   .0375137    -2.81   0.005    -.1787805   -.0317295
------------------------+----------------------------------------------------------------
zzscviolence_2_2a_lnvar |
                  _cons |  -1.572731   .0789553   -19.92   0.000     -1.72748   -1.417981
------------------------+----------------------------------------------------------------
zzscviolence_2_3a_mean  |
              civiceduc |  -.1821614   .0572909    -3.18   0.001    -.2944495   -.0698734
                hotline |  -.0707434   .0894307    -0.79   0.429    -.2460244    .1045376
                verdade |  -.1194943   .0639434    -1.87   0.062    -.2448211    .0058325
                    pr1 |   .1517879   .0763665     1.99   0.047     .0021124    .3014635
                    pr2 |   .1278663   .0569985     2.24   0.025     .0161512    .2395814
                    pr3 |   .0911782   .0710786     1.28   0.200    -.0481332    .2304897
                  _cons |  -.0934777   .0476503    -1.96   0.050    -.1868705   -.0000849
------------------------+----------------------------------------------------------------
zzscviolence_2_3a_lnvar |
                  _cons |  -1.424119   .1347698   -10.57   0.000    -1.688263   -1.159975
-----------------------------------------------------------------------------------------

 ( 1)  [zzscviolence_2_2a_mean]civiceduc - [zzscviolence_2_3a_mean]civiceduc = 0

           chi2(  1) =    0.60
         Prob > chi2 =    0.4375
.43753793

 ( 1)  [zzscviolence_2_2a_mean]hotline - [zzscviolence_2_3a_mean]hotline = 0

           chi2(  1) =    0.03
         Prob > chi2 =    0.8659
.86587459

 ( 1)  [zzscviolence_2_2a_mean]verdade - [zzscviolence_2_3a_mean]verdade = 0

           chi2(  1) =    0.43
         Prob > chi2 =    0.5129
.51292073

Linear regression                                      Number of obs =    1132
                                                       F( 25,   160) =    4.83
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0688
                                                       Root MSE      =  .44696

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zzscviolence |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1356833   .0407607    -3.33   0.001    -.2161816   -.0551849
     hotline |  -.0653493   .0447415    -1.46   0.146    -.1537094    .0230108
     verdade |  -.0729781   .0424755    -1.72   0.088    -.1568632    .0109069
         pr1 |   .1878565   .0579471     3.24   0.001     .0734166    .3022964
         pr2 |   .1716216   .0380564     4.51   0.000      .096464    .2467791
         pr3 |   .1182017   .0397449     2.97   0.003     .0397095    .1966939
        post |  -.0175619   .0410601    -0.43   0.669    -.0986517    .0635278
   post_miss |   .0877961   .0811069     1.08   0.281    -.0723821    .2479743
      health |   .0468468   .0311957     1.50   0.135    -.0147615    .1084552
 health_miss |  -.0990942   .0890644    -1.11   0.268    -.2749876    .0767992
         sex |   .0229925   .0287049     0.80   0.424    -.0336969    .0796818
         age |  -.0015148   .0012222    -1.24   0.217    -.0039285    .0008989
      single |   .1178125   .0399817     2.95   0.004     .0388526    .1967725
       divor |  -.0290087   .1618645    -0.18   0.858    -.3486751    .2906577
     protest |  -.0202614   .0285242    -0.71   0.479    -.0765938     .036071
         com |  -.0153225   .0722095    -0.21   0.832    -.1579291     .127284
        prof |   .2109163   .1714739     1.23   0.220    -.1277277    .5495604
         tea |   .0551554    .075537     0.73   0.466    -.0940227    .2043336
     comform |   .0152242    .120163     0.13   0.899    -.2220859    .2525342
         dom |   .0329631    .042399     0.78   0.438    -.0507706    .1166969
    econfood |   .0182432   .0099955     1.83   0.070    -.0014971    .0379834
       house |  -.0160967    .032565    -0.49   0.622    -.0804094    .0482161
      llomue |   .0570463   .0643012     0.89   0.376    -.0699422    .1840349
     chitsua |  -.0135763   .0968457    -0.14   0.889    -.2048371    .1776845
      living |  -.0042072   .0129269    -0.33   0.745    -.0297366    .0213221
       _cons |  -.1277717   .0768989    -1.66   0.099    -.2796395    .0240961
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
zzscviolence |       273    .0041755    .5011654  -.7026088   2.511753
.00417545

 ( 1)  civiceduc - hotline = 0

       F(  1,   160) =    3.31
            Prob > F =    0.0706
.07062402

 ( 1)  civiceduc - verdade = 0

       F(  1,   160) =    3.69
            Prob > F =    0.0566
.05659521

 ( 1)  hotline - verdade = 0

       F(  1,   160) =    0.04
            Prob > F =    0.8421
.84209991

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,   160) =    3.96
            Prob > F =    0.0093
.00927361

Linear regression                                      Number of obs =     962
                                                       F( 25,   160) =    3.45
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0659
                                                       Root MSE      =  .45421

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zzscviolence |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1228063   .0428321    -2.87   0.005    -.2073953   -.0382172
     hotline |  -.0510357   .0476582    -1.07   0.286    -.1451561    .0430846
     verdade |  -.0642169   .0435001    -1.48   0.142    -.1501253    .0216916
         pr1 |    .195787   .0571398     3.43   0.001     .0829415    .3086326
         pr2 |   .1772698   .0448787     3.95   0.000     .0886388    .2659009
         pr3 |   .1180895   .0448035     2.64   0.009     .0296071     .206572
        post |  -.0188906   .0480293    -0.39   0.695    -.1137437    .0759625
   post_miss |   .1214635   .0807484     1.50   0.134    -.0380066    .2809336
      health |   .0269853   .0349582     0.77   0.441    -.0420538    .0960243
 health_miss |  -.1363432   .0891773    -1.53   0.128    -.3124595    .0397732
         sex |   .0317366   .0325512     0.97   0.331    -.0325489    .0960221
         age |  -.0019982   .0012826    -1.56   0.121    -.0045312    .0005348
      single |   .1312097   .0459597     2.85   0.005     .0404439    .2219756
       divor |  -.1521694   .0782789    -1.94   0.054    -.3067625    .0024237
     protest |   -.026439   .0322857    -0.82   0.414    -.0902001    .0373221
         com |   .0126006   .0779354     0.16   0.872    -.1413142    .1665154
        prof |   .1792278   .1738543     1.03   0.304    -.1641174     .522573
         tea |   .0672586   .0894169     0.75   0.453    -.1093309    .2438482
     comform |    .072612    .146098     0.50   0.620    -.2159171    .3611411
         dom |   .0492785   .0503255     0.98   0.329    -.0501095    .1486664
    econfood |   .0097736   .0114873     0.85   0.396    -.0129128    .0324599
       house |   .0082844   .0361712     0.23   0.819      -.06315    .0797189
      llomue |   .0148702   .0691939     0.21   0.830     -.121781    .1515214
     chitsua |   .0165835   .1171066     0.14   0.888    -.2146906    .2478576
      living |   .0011589   .0140007     0.08   0.934    -.0264911    .0288088
       _cons |  -.1332571   .0814939    -1.64   0.104    -.2941995    .0276853
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     962
-------------+------------------------------           F( 25,   936) =    2.64
       Model |  13.6239799    25  .544959197           Prob > F      =  0.0000
    Residual |  193.104096   936  .206307795           R-squared     =  0.0659
-------------+------------------------------           Adj R-squared =  0.0410
       Total |  206.728076   961  .215117665           Root MSE      =  .45421

------------------------------------------------------------------------------
zzscviolence |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.1228063   .0421174    -2.92   0.004    -.2054617   -.0401509
     hotline |  -.0510357   .0418432    -1.22   0.223     -.133153    .0310816
     verdade |  -.0642169    .042776    -1.50   0.134    -.1481649    .0197312
         pr1 |    .195787   .0504505     3.88   0.000     .0967779    .2947962
         pr2 |   .1772698    .049199     3.60   0.000     .0807167     .273823
         pr3 |   .1180895   .0480339     2.46   0.014      .023823    .2123561
        post |  -.0188906   .0509653    -0.37   0.711      -.11891    .0811289
   post_miss |   .1214635   .0845724     1.44   0.151    -.0445099    .2874369
      health |   .0269853   .0351429     0.77   0.443    -.0419827    .0959533
 health_miss |  -.1363432   .0940541    -1.45   0.147    -.3209245    .0482382
         sex |   .0317366   .0316225     1.00   0.316    -.0303225    .0937958
         age |  -.0019982   .0012215    -1.64   0.102    -.0043954     .000399
      single |   .1312097   .0408554     3.21   0.001     .0510309    .2113886
       divor |  -.1521694   .1751801    -0.87   0.385    -.4959607    .1916218
     protest |   -.026439    .036029    -0.73   0.463    -.0971459    .0442679
         com |   .0126006   .0699555     0.18   0.857    -.1246872    .1498884
        prof |   .1792278   .1200225     1.49   0.136    -.0563165    .4147722
         tea |   .0672586   .0730231     0.92   0.357    -.0760493    .2105666
     comform |    .072612    .140232     0.52   0.605    -.2025936    .3478176
         dom |   .0492785   .0455142     1.08   0.279    -.0400433    .1386002
    econfood |   .0097736   .0130688     0.75   0.455     -.015874    .0354211
       house |   .0082844   .0430954     0.19   0.848    -.0762903    .0928592
      llomue |   .0148702   .0594093     0.25   0.802    -.1017207    .1314611
     chitsua |   .0165835   .1354027     0.12   0.903    -.2491446    .2823116
      living |   .0011589   .0148232     0.08   0.938    -.0279317    .0302494
       _cons |  -.1332571   .0812088    -1.64   0.101    -.2926295    .0261153
------------------------------------------------------------------------------

Linear regression                                      Number of obs =     443
                                                       F( 25,   151) =    3.61
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1027
                                                       Root MSE      =  .46445

                                   (Std. Err. adjusted for 152 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
zzscviolence |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.177645    .060633    -2.93   0.004    -.2974437   -.0578463
     hotline |  -.1198928    .064174    -1.87   0.064    -.2466877    .0069021
     verdade |  -.0835582   .0773076    -1.08   0.281    -.2363025    .0691861
         pr1 |    .164572   .0851255     1.93   0.055    -.0036188    .3327629
         pr2 |   .1467339   .0640719     2.29   0.023     .0201406    .2733271
         pr3 |   .1500046   .0698448     2.15   0.033     .0120054    .2880038
        post |   .0206577   .0539597     0.38   0.702    -.0859559    .1272712
   post_miss |   .0635858    .108944     0.58   0.560    -.1516656    .2788372
      health |   .0990393   .0559189     1.77   0.079    -.0114451    .2095237
 health_miss |  -.1000394   .1635913    -0.61   0.542     -.423263    .2231841
         sex |   .0283359   .0429001     0.66   0.510    -.0564262    .1130979
         age |  -.0002604   .0021865    -0.12   0.905    -.0045806    .0040598
      single |   .1417108   .0607052     2.33   0.021     .0217695    .2616521
       divor |   .3028045   .4025217     0.75   0.453    -.4924975    1.098106
     protest |  -.0034852   .0489835    -0.07   0.943    -.1002667    .0932962
         com |   .0865887    .086926     1.00   0.321    -.0851596     .258337
        prof |   .2057267   .2524418     0.81   0.416    -.2930476    .7045009
         tea |   .0322498   .1226073     0.26   0.793    -.2099976    .2744971
     comform |  -.1347024   .0872146    -1.54   0.125    -.3070209    .0376162
         dom |   .0189539    .079673     0.24   0.812     -.138464    .1763718
    econfood |   .0187428   .0158712     1.18   0.239    -.0126154     .050101
       house |  -.1028242   .0537736    -1.91   0.058    -.2090699    .0034215
      llomue |   .0641871   .1111424     0.58   0.564     -.155408    .2837822
     chitsua |  -.1241163   .0839864    -1.48   0.142    -.2900566     .041824
      living |  -.0418652   .0199207    -2.10   0.037    -.0812245    -.002506
       _cons |  -.0370345   .1229003    -0.30   0.764    -.2798607    .2057918
------------------------------------------------------------------------------

      Source |       SS       df       MS              Number of obs =     443
-------------+------------------------------           F( 25,   417) =    1.91
       Model |  10.2963343    25  .411853371           Prob > F      =  0.0057
    Residual |   89.950883   417  .215709552           R-squared     =  0.1027
-------------+------------------------------           Adj R-squared =  0.0489
       Total |  100.247217   442  .226803659           Root MSE      =  .46445

------------------------------------------------------------------------------
zzscviolence |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   -.177645   .0730446    -2.43   0.015    -.3212266   -.0340634
     hotline |  -.1198928     .06812    -1.76   0.079    -.2537941    .0140085
     verdade |  -.0835582   .0718622    -1.16   0.246    -.2248156    .0576992
         pr1 |    .164572   .0786306     2.09   0.037     .0100103    .3191338
         pr2 |   .1467339   .0745754     1.97   0.050     .0001432    .2933245
         pr3 |   .1500046   .0736628     2.04   0.042     .0052079    .2948012
        post |   .0206577   .0663423     0.31   0.756    -.1097494    .1510647
   post_miss |   .0635858    .109859     0.58   0.563    -.1523607    .2795323
      health |   .0990393   .0565076     1.75   0.080     -.012036    .2101146
 health_miss |  -.1000394   .1613862    -0.62   0.536    -.4172713    .2171924
         sex |   .0283359   .0478142     0.59   0.554    -.0656511    .1223228
         age |  -.0002604   .0019369    -0.13   0.893    -.0040677    .0035469
      single |   .1417108   .0587732     2.41   0.016     .0261821    .2572396
       divor |   .3028045   .2734149     1.11   0.269    -.2346388    .8402478
     protest |  -.0034852   .0567805    -0.06   0.951     -.115097    .1081265
         com |   .0865887   .1172981     0.74   0.461    -.1439806     .317158
        prof |   .2057267   .1603827     1.28   0.200    -.1095327    .5209861
         tea |   .0322498   .0979768     0.33   0.742    -.1603402    .2248398
     comform |  -.1347024   .1952296    -0.69   0.491    -.5184592    .2490545
         dom |   .0189539   .0677446     0.28   0.780    -.1142095    .1521173
    econfood |   .0187428   .0206076     0.91   0.364    -.0217648    .0592505
       house |  -.1028242   .0653988    -1.57   0.117    -.2313767    .0257283
      llomue |   .0641871   .0932335     0.69   0.492    -.1190791    .2474533
     chitsua |  -.1241163   .2167518    -0.57   0.567    -.5501786     .301946
      living |  -.0418652   .0218202    -1.92   0.056    -.0847564     .001026
       _cons |  -.0370345    .120965    -0.31   0.760    -.2748115    .2007426
------------------------------------------------------------------------------

Simultaneous results for zzscviolence_3_2a, zzscviolence_3_3a

                                                  Number of obs   =       1132

                                              (Std. Err. adjusted for 161 clusters in ea)
-----------------------------------------------------------------------------------------
                        |               Robust
                        |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
zzscviolence_3_2a_mean  |
              civiceduc |  -.1228063   .0422713    -2.91   0.004    -.2056564   -.0399561
                hotline |  -.0510357   .0470343    -1.09   0.278    -.1432212    .0411497
                verdade |  -.0642169   .0429306    -1.50   0.135    -.1483593    .0199255
                    pr1 |    .195787   .0563917     3.47   0.001     .0852614    .3063127
                    pr2 |   .1772698   .0442911     4.00   0.000     .0904608    .2640789
                    pr3 |   .1180895   .0442169     2.67   0.008     .0314261     .204753
                   post |  -.0188906   .0474004    -0.40   0.690    -.1117937    .0740126
              post_miss |   .1214635   .0796911     1.52   0.127    -.0347283    .2776552
                 health |   .0269853   .0345005     0.78   0.434    -.0406345    .0946051
            health_miss |  -.1363432   .0880097    -1.55   0.121     -.308839    .0361526
                    sex |   .0317366    .032125     0.99   0.323    -.0312273    .0947005
                    age |  -.0019982   .0012658    -1.58   0.114    -.0044791    .0004827
                 single |   .1312097   .0453579     2.89   0.004     .0423098    .2201096
                  divor |  -.1521694    .077254    -1.97   0.049    -.3035845   -.0007544
                protest |   -.026439    .031863    -0.83   0.407    -.0888893    .0360113
                    com |   .0126006    .076915     0.16   0.870    -.1381501    .1633513
                   prof |   .1792278   .1715781     1.04   0.296     -.157059    .5155146
                    tea |   .0672586   .0882461     0.76   0.446    -.1057006    .2402179
                comform |    .072612   .1441851     0.50   0.615    -.2099856    .3552096
                    dom |   .0492785   .0496666     0.99   0.321    -.0480663    .1466232
               econfood |   .0097736   .0113369     0.86   0.389    -.0124464    .0319936
                  house |   .0082844   .0356976     0.23   0.816    -.0616815    .0782504
                 llomue |   .0148702    .068288     0.22   0.828    -.1189718    .1487122
                chitsua |   .0165835   .1155734     0.14   0.886    -.2099361    .2431031
                 living |   .0011589   .0138174     0.08   0.933    -.0259226    .0282404
                  _cons |  -.1332571   .0804269    -1.66   0.098    -.2908909    .0243767
------------------------+----------------------------------------------------------------
zzscviolence_3_2a_lnvar |
                  _cons |  -1.578386   .0763013   -20.69   0.000    -1.727934   -1.428838
------------------------+----------------------------------------------------------------
zzscviolence_3_3a_mean  |
              civiceduc |   -.177645   .0588824    -3.02   0.003    -.2930525   -.0622375
                hotline |  -.1198928   .0623212    -1.92   0.054    -.2420401    .0022545
                verdade |  -.0835582   .0750756    -1.11   0.266    -.2307036    .0635872
                    pr1 |    .164572   .0826678     1.99   0.047     .0025462    .3265979
                    pr2 |   .1467339   .0622221     2.36   0.018     .0247809    .2686868
                    pr3 |   .1500046   .0678282     2.21   0.027     .0170637    .2829454
                   post |   .0206577   .0524018     0.39   0.693     -.082048    .1233633
              post_miss |   .0635858   .1057986     0.60   0.548    -.1437756    .2709472
                 health |   .0990393   .0543044     1.82   0.068    -.0073953    .2054739
            health_miss |  -.1000394   .1588681    -0.63   0.529    -.4114153    .2113364
                    sex |   .0283359   .0416615     0.68   0.496    -.0533192    .1099909
                    age |  -.0002604   .0021234    -0.12   0.902    -.0044222    .0039014
                 single |   .1417108   .0589525     2.40   0.016      .026166    .2572556
                  divor |   .3028045   .3909002     0.77   0.439    -.4633457    1.068955
                protest |  -.0034852   .0475692    -0.07   0.942    -.0967192    .0897487
                    com |   .0865887   .0844163     1.03   0.305    -.0788642    .2520416
                   prof |   .2057267   .2451533     0.84   0.401     -.274765    .6862184
                    tea |   .0322498   .1190674     0.27   0.787     -.201118    .2656176
                comform |  -.1347024   .0846966    -1.59   0.112    -.3007046    .0312998
                    dom |   .0189539   .0773727     0.24   0.806    -.1326939    .1706017
               econfood |   .0187428   .0154129     1.22   0.224     -.011466    .0489516
                  house |  -.1028242    .052221    -1.97   0.049    -.2051755   -.0004729
                 llomue |   .0641871   .1079335     0.59   0.552    -.1473588    .2757329
                chitsua |  -.1241163   .0815616    -1.52   0.128    -.2839741    .0357415
                 living |  -.0418652   .0193455    -2.16   0.030    -.0797818   -.0039487
                  _cons |  -.0370345   .1193519    -0.31   0.756      -.27096     .196891
------------------------+----------------------------------------------------------------
zzscviolence_3_3a_lnvar |
                  _cons |  -1.533822     .10953   -14.00   0.000    -1.748497   -1.319148
-----------------------------------------------------------------------------------------

 ( 1)  [zzscviolence_3_2a_mean]civiceduc - [zzscviolence_3_3a_mean]civiceduc = 0

           chi2(  1) =    1.00
         Prob > chi2 =    0.3182
.31820337

 ( 1)  [zzscviolence_3_2a_mean]hotline - [zzscviolence_3_3a_mean]hotline = 0

           chi2(  1) =    1.25
         Prob > chi2 =    0.2639
.26386202

 ( 1)  [zzscviolence_3_2a_mean]verdade - [zzscviolence_3_3a_mean]verdade = 0

           chi2(  1) =    0.08
         Prob > chi2 =    0.7822
.78220442

. 
. matrix define means=(m_zzscviolence_2_1, m_zzscviolence_3_1 \ t_zzscviolence_2_1_1, t_zzscviol
> ence_3_1_1 \ t_zzscviolence_2_1_2, t_zzscviolence_3_1_2 \ t_zzscviolence_2_1_3, t_zzscviolence
> _3_1_3 \ t_zzscviolence_2_1_4, t_zzscviolence_3_1_4 \ t_zzscviolence_2_5, t_zzscviolence_3_5 \
>  t_zzscviolence_2_6, t_zzscviolence_3_6 \ t_zzscviolence_2_7, t_zzscviolence_3_7)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_survey.xml") append sheet("violen
> ce") 


note: results saved to outputregs_survey.xml

. xml_tab $list2, save("outputregs_survey.xml") append sheet("violence stats") 


note: results saved to outputregs_survey.xml

. estimates clear

. 
. ***********************************
. *****  OA TABLE 7: AB CHECKS  *****
. ***********************************
. 
. clear all

. set more off

. 
. *R3
. 
. use moz_r3_data.dta, replace

. 
. *province
. 
. gen ourprov=0

. replace ourprov=1 if REGION==300 | REGION==302 | REGION==307 | REGION==310
(464 real changes made)

. tab REGION if ourprov==1

    Province or |
         region |      Freq.     Percent        Cum.
----------------+-----------------------------------
Maputo province |         64       13.79       13.79
           Gaza |         80       17.24       31.03
       Zambezia |        224       48.28       79.31
   Cabo Delgado |         96       20.69      100.00
----------------+-----------------------------------
          Total |        464      100.00

. 
. *district
. 
. encode DISTRICT, gen(dist)

. tab dist

           District |      Freq.     Percent        Cum.
--------------------+-----------------------------------
       ALTO-MOLOCUE |         16        1.34        1.34
            ANCUABE |          8        0.67        2.00
            ANGOCHE |         32        2.67        4.67
            ANGONIA |         16        1.34        6.01
              BARUE |          8        0.67        6.68
              BEIRA |         32        2.67        9.35
             BILENE |         16        1.34       10.68
               BUZI |          8        0.67       11.35
       CAHORA BASSA |         16        1.34       12.69
               CAIA |         16        1.34       14.02
           CHANGARA |          8        0.67       14.69
          CHIBABAVA |          8        0.67       15.36
            CHIBUTO |          8        0.67       16.03
            CHIMOIO |         16        1.34       17.36
             CHIURE |          8        0.67       18.03
             CHIUTA |          8        0.67       18.70
             CHOKWE |          8        0.67       19.37
   CIDADE DA MAXIXE |         16        1.34       20.70
CIDADE DE INHAMBANE |          8        0.67       21.37
  CIDADE DE NAMPULA |         32        2.67       24.04
    CIDADE DE PEMBA |         16        1.34       25.38
CIDADE DE QUELIMANE |         32        2.67       28.05
     CIDADE DE TETE |         16        1.34       29.38
  CIDADE DE XAI-XAI |          8        0.67       30.05
             CUAMBA |         15        1.25       31.30
     D. URBANO N� 1 |          8        0.67       31.97
     D. URBANO N� 4 |         32        2.67       34.64
     D. URBANO N� 5 |          8        0.67       35.31
          D.URB N�2 |          8        0.67       35.98
          D.URB.N�3 |         24        2.00       37.98
              DONDO |         16        1.34       39.32
            GONDOLA |         16        1.34       40.65
          GORONGOSA |          8        0.67       41.32
              GUIJA |         16        1.34       42.65
               GURO |          8        0.67       43.32
              GURUE |         32        2.67       45.99
            HOMOINE |          8        0.67       46.66
                ILE |         16        1.34       48.00
 ILHA DE MO�AMBIQUE |          8        0.67       48.66
          INHASSORO |          8        0.67       49.33
           LICHINGA |         23        1.92       51.25
             LUGELA |         16        1.34       52.59
            MACOMIA |          8        0.67       53.26
             MAGUDE |          8        0.67       53.92
             MALEMA |          8        0.67       54.59
             MAMUMO |          8        0.67       55.26
         MANDLACAZE |          8        0.67       55.93
            MANHI�A |         16        1.34       57.26
             MANICA |         16        1.34       58.60
         MARRACUENE |          8        0.67       59.27
           MARROMEU |          8        0.67       59.93
           MASSINGA |          8        0.67       60.60
             MATOLA |         24        2.00       62.60
         MECANHELAS |         16        1.34       63.94
            MECONTA |          8        0.67       64.61
           MECUBURI |         16        1.34       65.94
              MEMBA |         16        1.34       67.28
            MILANGE |         32        2.67       69.95
             MOAMBA |          8        0.67       70.62
            MOATIZE |         16        1.34       71.95
             MOCUBA |         40        3.34       75.29
  MOC�MBOA DA PRAIA |         16        1.34       76.63
          MOGOVOLAS |         16        1.34       77.96
               MOMA |         16        1.34       79.30
          MONTEPUEZ |         16        1.34       80.63
         MORRUMBALA |         16        1.34       81.97
         MORRUMBENE |          8        0.67       82.64
              MUEDA |          8        0.67       83.31
           MUIDUMBE |          8        0.67       83.97
          MURRUPULA |          8        0.67       84.64
          MUSSURIZE |          8        0.67       85.31
           MUTARARA |          8        0.67       85.98
       NACALA PORTO |         24        2.00       87.98
          NAMACURRA |          8        0.67       88.65
             NAMAPA |         24        2.00       90.65
         NHAMATANDA |          8        0.67       91.32
          NICOADALA |         16        1.34       92.65
             RAPALE |         24        2.00       94.66
              SANGA |          8        0.67       95.33
        SUSSUNDENGA |          8        0.67       95.99
         VILANCULOS |         16        1.34       97.33
            XAI-XAI |         16        1.34       98.66
             ZAVALA |         16        1.34      100.00
--------------------+-----------------------------------
              Total |      1,198      100.00

. 
. *ea
. 
. egen ea_full=concat(URBRUR dist)

. encode ea_full, gen(ea)

. drop ea_full

. tab ea

         ea |      Freq.     Percent        Cum.
------------+-----------------------------------
         11 |         16        1.34        1.34
        114 |         16        1.34        2.67
        117 |          8        0.67        3.34
        118 |         16        1.34        4.67
        119 |          8        0.67        5.34
        120 |         32        2.67        8.01
        121 |         16        1.34        9.35
        122 |         32        2.67       12.02
        123 |         16        1.34       13.36
        124 |          8        0.67       14.02
        125 |          8        0.67       14.69
        126 |          8        0.67       15.36
        127 |         32        2.67       18.03
        128 |          8        0.67       18.70
        129 |          8        0.67       19.37
         13 |         16        1.34       20.70
        130 |         24        2.00       22.70
        131 |          8        0.67       23.37
        134 |          8        0.67       24.04
        136 |          8        0.67       24.71
        139 |          8        0.67       25.38
        141 |         16        1.34       26.71
        143 |          8        0.67       27.38
        145 |          8        0.67       28.05
        148 |          8        0.67       28.71
        149 |          8        0.67       29.38
         15 |          8        0.67       30.05
        153 |         16        1.34       31.39
        155 |          8        0.67       32.05
        158 |          8        0.67       32.72
         16 |         32        2.67       35.39
        160 |          8        0.67       36.06
        161 |         24        2.00       38.06
        162 |          8        0.67       38.73
        165 |          8        0.67       39.40
         17 |          8        0.67       40.07
        173 |         24        2.00       42.07
        181 |          8        0.67       42.74
         19 |          8        0.67       43.41
        210 |         16        1.34       44.74
        211 |          8        0.67       45.41
        212 |          8        0.67       46.08
        213 |          8        0.67       46.74
        215 |          8        0.67       47.41
        216 |          8        0.67       48.08
         22 |          8        0.67       48.75
        225 |          7        0.58       49.33
         23 |         16        1.34       50.67
        231 |          8        0.67       51.34
        232 |         16        1.34       52.67
        233 |          8        0.67       53.34
        234 |          8        0.67       54.01
        235 |          8        0.67       54.67
        236 |         24        2.00       56.68
        237 |          8        0.67       57.35
        238 |         16        1.34       58.68
         24 |         16        1.34       60.02
        240 |          8        0.67       60.68
        241 |          7        0.58       61.27
        242 |         16        1.34       62.60
        244 |          8        0.67       63.27
        246 |          8        0.67       63.94
        247 |          8        0.67       64.61
        248 |          8        0.67       65.28
        249 |          8        0.67       65.94
        250 |          8        0.67       66.61
        251 |          8        0.67       67.28
        252 |          8        0.67       67.95
        253 |          8        0.67       68.61
        254 |         16        1.34       69.95
        256 |         16        1.34       71.29
        257 |         16        1.34       72.62
        258 |         24        2.00       74.62
        259 |          8        0.67       75.29
        260 |          8        0.67       75.96
        261 |         16        1.34       77.30
        262 |          8        0.67       77.96
        263 |         16        1.34       79.30
        264 |         16        1.34       80.63
        265 |          8        0.67       81.30
        266 |         16        1.34       82.64
        267 |          8        0.67       83.31
        268 |          8        0.67       83.97
        269 |          8        0.67       84.64
         27 |          8        0.67       85.31
        270 |          8        0.67       85.98
        271 |          8        0.67       86.64
        272 |          8        0.67       87.31
        274 |          8        0.67       87.98
        275 |         24        2.00       89.98
        276 |          8        0.67       90.65
        277 |         16        1.34       91.99
        278 |         24        2.00       93.99
        279 |          8        0.67       94.66
         28 |          8        0.67       95.33
        280 |          8        0.67       95.99
        281 |          8        0.67       96.66
        282 |         16        1.34       98.00
        283 |         16        1.34       99.33
         29 |          8        0.67      100.00
------------+-----------------------------------
      Total |      1,198      100.00

. 
. *gender
. 
. gen gender=CURRINT

. replace gender=0 if CURRINT==2
(600 real changes made)

. sum gender

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
      gender |      1198    .4991653    .5002081          0          1

. 
. *age
. 
. gen age=Q1

. replace age=. if age==998 | age==999 | age==-1
(53 real changes made, 53 to missing)

. sum age

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
         age |      1145    35.27162    13.76509         18         99

. gen midage=1

. replace midage=0 if age<30 | age>50
(696 real changes made)

. replace midage=. if age==.
(53 real changes made, 53 to missing)

. sum midage

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
      midage |      1145    .4384279    .4964112          0          1

. 
. *hhead
. 
. gen hhead=Q2

. replace hhead=. if hhead==9 | hhead==998 | hhead==-1
(52 real changes made, 52 to missing)

. sum hhead

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       hhead |      1146    .6143106    .4869702          0          1

. 
. *celluse N/A
. 
. *education (0-9)
. 
. gen educ=Q90

. replace educ=. if Q90==99 | Q90==998 | Q90==-1
(27 real changes made, 27 to missing)

. sum educ

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
        educ |      1171    2.248506    1.642045          0          9

. 
. *property
. 
. gen book=Q93A

. replace book=. if Q93A==9 | Q93A==998 | Q93A==-1
(22 real changes made, 22 to missing)

. sum book

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
        book |      1176    .5671769    .4956775          0          1

. 
. gen radio=Q93B

. replace radio=. if Q93B==9 | Q93B==998 | Q93B==-1
(8 real changes made, 8 to missing)

. sum radio

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       radio |      1190    .6621849     .473164          0          1

. 
. gen tv=Q93C

. replace tv=. if Q93C==9 | Q93C==998 | Q93C==-1
(27 real changes made, 27 to missing)

. sum tv

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
          tv |      1171    .1955594    .3968001          0          1

. 
. gen bike=Q93D

. replace bike=. if Q93D==9 | Q93D==998 | Q93D==-1
(13 real changes made, 13 to missing)

. sum bike

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
        bike |      1185    .3898734    .4879273          0          1

. 
. gen mbike=Q93E

. replace mbike=. if Q93E==9 | Q93E==998 | Q93E==-1
(12 real changes made, 12 to missing)

. sum mbike

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       mbike |      1186    .0303541    .1716322          0          1

. 
. gen motor=Q93F

. replace motor=. if Q93F==9 | Q93F==998 | Q93F==-1
(12 real changes made, 12 to missing)

. sum motor

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       motor |      1186     .044688    .2067052          0          1

. 
. *job
. 
. gen job=.
(1198 missing values generated)

. replace job=1 if Q94==2 | Q94==3 | Q94==4 | Q94==5
(266 real changes made)

. replace job=0 if Q94==0 | Q94==1
(919 real changes made)

. sum job

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
         job |      1185    .2244726    .4174107          0          1

. 
. *interest (0-3)
. 
. gen interest=Q16

. replace interest=. if interest==9 | interest==998 | interest==-1
(60 real changes made, 60 to missing)

. sum interest

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    interest |      1138    1.934974    1.076326          0          3

. 
. *turnout04
. 
. gen turnout=0

. replace turnout=1 if Q30==1
(961 real changes made)

. replace turnout=. if Q30==. | Q30==9 | Q30==998 | Q30==-1
(8 real changes made, 8 to missing)

. sum turnout

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     turnout |      1190     .807563    .3943801          0          1

. 
. keep ourprov DISTRICT ea gender age midage hhead educ radio tv mbike motor job interest turnou
> t

. 
. gen celluse=.
(1198 missing values generated)

. 
. replace motor=1 if mbike==1
(24 real changes made)

. drop mbike

. 
. label drop ea

. 
. save moz_r3_data_r, replace
(note: file moz_r3_data_r.dta not found)
file moz_r3_data_r.dta saved

. 
. *R4
. 
. use moz_r4_data.dta, replace

. 
. *province
. 
. capture gen ourprov=0

. replace ourprov=1 if REGION==540 | REGION==542 | REGION==547 | REGION==550
(472 real changes made)

. tab REGION if ourprov==1

 Province or |
      region |      Freq.     Percent        Cum.
-------------+-----------------------------------
      Maputo |         72       15.25       15.25
        Gaza |         72       15.25       30.51
    Zambezia |        232       49.15       79.66
Cabo Delgado |         96       20.34      100.00
-------------+-----------------------------------
       Total |        472      100.00

. 
. *district
. 
. encode DISTRICT, gen(dist)

. tab dist

           District |      Freq.     Percent        Cum.
--------------------+-----------------------------------
       ALTO MOLOCUE |         24        2.00        2.00
            ANCUABE |         16        1.33        3.33
            ANGOCHE |          8        0.67        4.00
            ANGONIA |         16        1.33        5.33
              BEIRA |         24        2.00        7.33
             BILENE |         16        1.33        8.67
              BOANE |         16        1.33       10.00
               CAIA |         16        1.33       11.33
           CHANGARA |         16        1.33       12.67
            CHIBUTO |          8        0.67       13.33
            CHIMOIO |         16        1.33       14.67
             CHIURE |         24        2.00       16.67
             CHIUTA |         16        1.33       18.00
             CHOKWE |         16        1.33       19.33
   CIDADE DA MATOLA |         16        1.33       20.67
CIDADE DE INHAMBANE |          8        0.67       21.33
  CIDADE DE NAMPULA |         24        2.00       23.33
  CIDADE DE XAI-XAI |          8        0.67       24.00
             CUAMBA |         24        2.00       26.00
  DISTRITO URBANO 2 |         16        1.33       27.33
DISTRITO URBANO N 3 |          8        0.67       28.00
DISTRITO URBANO N 4 |         16        1.33       29.33
DISTRITO URBANO N 5 |         16        1.33       30.67
              DONDO |         24        2.00       32.67
               GILE |         16        1.33       34.00
            GONDOLA |         24        2.00       36.00
              GUIJA |          8        0.67       36.67
              GURUE |         24        2.00       38.67
            HOMOINE |         16        1.33       40.00
                ILE |         16        1.33       41.33
            JANGAMO |         16        1.33       42.67
           LICHINGA |          8        0.67       43.33
            MACANGA |         16        1.33       44.67
            MACHAZE |         16        1.33       46.00
   MAGANJA DA COSTA |         16        1.33       47.33
             MALEMA |         16        1.33       48.67
           MANDIMBA |         16        1.33       50.00
            MANHICA |         16        1.33       51.33
     MAPUTO-CATEMBE |          8        0.67       52.00
           MARROMEU |         24        2.00       54.00
          MASSINGIR |         16        1.33       55.33
             MATOLA |          8        0.67       56.00
      MATOLA CIDADE |         16        1.33       57.33
             MAXIXE |          8        0.67       58.00
            MECONTA |          8        0.67       58.67
           MECUBURI |         16        1.33       60.00
              MEMBA |         16        1.33       61.33
           METARICA |         16        1.33       62.67
            MILANGE |         32        2.67       65.33
            MOATIZE |         16        1.33       66.67
  MOCIMBOA DA PRAIA |          8        0.67       67.33
             MOCUBA |         24        2.00       69.33
          MOGOVOLAS |         16        1.33       70.67
               MOMA |         16        1.33       72.00
             MONAPO |         24        2.00       74.00
          MONTEPUEZ |         16        1.33       75.33
         MORRUMBALA |         24        2.00       77.33
         MORRUMBENE |         16        1.33       78.67
          MOSSURIZE |         16        1.33       80.00
            MUECATE |         16        1.33       81.33
           MUTARARA |         16        1.33       82.67
     NACALA - PORTO |          8        0.67       83.33
            NACAROA |         16        1.33       84.67
          NAMACURRA |         16        1.33       86.00
       NAMAPA-ERATI |         24        2.00       88.00
           NAMARROI |         16        1.33       89.33
             NAMUNO |         16        1.33       90.67
         NHAMATANDA |         16        1.33       92.00
              PALMA |          8        0.67       92.67
             PEBANE |         16        1.33       94.00
       PEMBA CIDADE |          8        0.67       94.67
          QUELIMANE |          8        0.67       95.33
             RIBAUE |         16        1.33       96.67
        SUSSUNDENGA |          8        0.67       97.33
               TETE |         16        1.33       98.67
          VILANKULO |          8        0.67       99.33
             ZAVALA |          8        0.67      100.00
--------------------+-----------------------------------
              Total |      1,200      100.00

. 
. *ea
. 
. gen ea=EANUMB_AB

. tab ea

         ea |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |          8        0.67        0.67
          2 |          8        0.67        1.33
          3 |          8        0.67        2.00
          4 |          8        0.67        2.67
          5 |          8        0.67        3.33
          6 |          8        0.67        4.00
          7 |          8        0.67        4.67
          8 |          8        0.67        5.33
          9 |          8        0.67        6.00
         10 |          8        0.67        6.67
         11 |          8        0.67        7.33
         12 |          8        0.67        8.00
         13 |          8        0.67        8.67
         14 |          8        0.67        9.33
         15 |          8        0.67       10.00
         16 |          8        0.67       10.67
         17 |          8        0.67       11.33
         18 |          8        0.67       12.00
         19 |          8        0.67       12.67
         20 |          8        0.67       13.33
         21 |          8        0.67       14.00
         22 |          8        0.67       14.67
         23 |          8        0.67       15.33
         24 |          8        0.67       16.00
         25 |          8        0.67       16.67
         26 |          8        0.67       17.33
         27 |          8        0.67       18.00
         28 |          8        0.67       18.67
         29 |          8        0.67       19.33
         30 |          8        0.67       20.00
         31 |          8        0.67       20.67
         32 |          8        0.67       21.33
         33 |          8        0.67       22.00
         34 |          8        0.67       22.67
         35 |          8        0.67       23.33
         36 |          8        0.67       24.00
         37 |          8        0.67       24.67
         38 |          8        0.67       25.33
         39 |          8        0.67       26.00
         40 |          8        0.67       26.67
         41 |          8        0.67       27.33
         42 |          8        0.67       28.00
         43 |          8        0.67       28.67
         44 |          8        0.67       29.33
         45 |          8        0.67       30.00
         46 |          8        0.67       30.67
         47 |          8        0.67       31.33
         48 |          8        0.67       32.00
         49 |          8        0.67       32.67
         50 |          8        0.67       33.33
         51 |          8        0.67       34.00
         52 |          8        0.67       34.67
         53 |          8        0.67       35.33
         54 |          8        0.67       36.00
         55 |          8        0.67       36.67
         56 |          8        0.67       37.33
         57 |          8        0.67       38.00
         58 |          8        0.67       38.67
         59 |          8        0.67       39.33
         60 |          8        0.67       40.00
         61 |          8        0.67       40.67
         62 |          8        0.67       41.33
         63 |          8        0.67       42.00
         64 |          8        0.67       42.67
         65 |          8        0.67       43.33
         66 |          8        0.67       44.00
         67 |          8        0.67       44.67
         68 |          8        0.67       45.33
         69 |          8        0.67       46.00
         70 |          8        0.67       46.67
         71 |          8        0.67       47.33
         72 |          8        0.67       48.00
         73 |          8        0.67       48.67
         74 |          8        0.67       49.33
         75 |          8        0.67       50.00
         76 |          8        0.67       50.67
         77 |          8        0.67       51.33
         78 |          8        0.67       52.00
         79 |          8        0.67       52.67
         80 |          8        0.67       53.33
         81 |          8        0.67       54.00
         82 |          8        0.67       54.67
         83 |          8        0.67       55.33
         84 |          8        0.67       56.00
         85 |          8        0.67       56.67
         86 |          8        0.67       57.33
         87 |          8        0.67       58.00
         88 |          8        0.67       58.67
         89 |          8        0.67       59.33
         90 |          8        0.67       60.00
         91 |          8        0.67       60.67
         92 |          8        0.67       61.33
         93 |          8        0.67       62.00
         94 |          8        0.67       62.67
         95 |          8        0.67       63.33
         96 |          8        0.67       64.00
         97 |          8        0.67       64.67
         98 |          8        0.67       65.33
         99 |          8        0.67       66.00
        100 |          8        0.67       66.67
        101 |          8        0.67       67.33
        102 |          8        0.67       68.00
        103 |          8        0.67       68.67
        104 |          8        0.67       69.33
        105 |          8        0.67       70.00
        106 |          8        0.67       70.67
        107 |          8        0.67       71.33
        108 |          8        0.67       72.00
        109 |          8        0.67       72.67
        110 |          8        0.67       73.33
        111 |          8        0.67       74.00
        112 |          8        0.67       74.67
        113 |          8        0.67       75.33
        114 |          8        0.67       76.00
        115 |          8        0.67       76.67
        116 |          8        0.67       77.33
        117 |          8        0.67       78.00
        118 |          8        0.67       78.67
        119 |          8        0.67       79.33
        120 |          8        0.67       80.00
        121 |          8        0.67       80.67
        122 |          8        0.67       81.33
        123 |          8        0.67       82.00
        124 |          8        0.67       82.67
        125 |          8        0.67       83.33
        126 |          8        0.67       84.00
        127 |          8        0.67       84.67
        128 |          8        0.67       85.33
        129 |          8        0.67       86.00
        130 |          8        0.67       86.67
        131 |          8        0.67       87.33
        132 |          8        0.67       88.00
        133 |          8        0.67       88.67
        134 |          8        0.67       89.33
        135 |          8        0.67       90.00
        136 |          8        0.67       90.67
        137 |          8        0.67       91.33
        138 |          8        0.67       92.00
        139 |          8        0.67       92.67
        140 |          8        0.67       93.33
        141 |          8        0.67       94.00
        142 |          8        0.67       94.67
        143 |          8        0.67       95.33
        144 |          8        0.67       96.00
        145 |          8        0.67       96.67
        146 |          8        0.67       97.33
        147 |          8        0.67       98.00
        148 |          8        0.67       98.67
        149 |          8        0.67       99.33
        150 |          8        0.67      100.00
------------+-----------------------------------
      Total |      1,200      100.00

. 
. *gender
. 
. gen gender=THISINT

. replace gender=0 if THISINT==2
(600 real changes made)

. sum gender

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
      gender |      1200          .5    .5002085          0          1

. 
. *age
. 
. gen age=Q1

. replace age=. if age==999
(53 real changes made, 53 to missing)

. sum age

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
         age |      1147    30.64429    11.95384         18         85

. gen midage=1

. replace midage=0 if age<30 | age>50
(804 real changes made)

. replace midage=. if age==.
(53 real changes made, 53 to missing)

. sum midage

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
      midage |      1147    .3452485    .4756566          0          1

. 
. *hhead
. 
. gen hhead=Q2

. replace hhead=. if hhead==9 | hhead==-1
(12 real changes made, 12 to missing)

. sum hhead

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       hhead |      1188    .5151515    .4999809          0          1

. 
. *celluse
. 
. capture drop celluse

. gen celluse=0

. replace celluse=1 if Q88A>2
(578 real changes made)

. replace celluse=. if Q88A==9
(3 real changes made, 3 to missing)

. sum celluse

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     celluse |      1197    .4803676    .4998232          0          1

. 
. *education (0-9)
. 
. gen educ=Q89

. replace educ=. if Q89==99 | Q89==998 | Q89==-1
(8 real changes made, 8 to missing)

. tab educ

       educ |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        108        9.06        9.06
          1 |         20        1.68       10.74
          2 |        343       28.78       39.51
          3 |        202       16.95       56.46
          4 |        333       27.94       84.40
          5 |        130       10.91       95.30
          6 |         28        2.35       97.65
          7 |         22        1.85       99.50
          8 |          6        0.50      100.00
------------+-----------------------------------
      Total |      1,192      100.00

. 
. *property
. 
. gen radio=Q92A

. replace radio=. if Q92A==9 | Q92A==998 | Q92A==-1
(0 real changes made)

. sum radio

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       radio |      1200       .7025    .4573489          0          1

. 
. gen tv=Q92B

. replace tv=. if Q92B==9 | Q92B==998 | Q92B==-1
(1 real change made, 1 to missing)

. sum tv

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
          tv |      1199    .3311093    .4708087          0          1

. 
. gen motor=Q92C

. replace motor=. if Q92C==9 | Q92C==998 | Q92C==-1
(2 real changes made, 2 to missing)

. sum motor

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       motor |      1198    .1494157    .3566466          0          1

. 
. *job
. 
. gen job=.
(1200 missing values generated)

. replace job=1 if Q94==2 | Q94==3 | Q94==4 | Q94==5
(298 real changes made)

. replace job=0 if Q94==0 | Q94==1
(896 real changes made)

. sum job

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
         job |      1194    .2495812     .432952          0          1

. 
. *interest (0-3)
. 
. gen interest=Q13

. replace interest=. if interest==9
(46 real changes made, 46 to missing)

. tab interest

   interest |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        250       21.66       21.66
          1 |        114        9.88       31.54
          2 |        305       26.43       57.97
          3 |        485       42.03      100.00
------------+-----------------------------------
      Total |      1,154      100.00

. 
. *turnout04
. 
. gen turnout=0

. replace turnout=1 if Q23D==1
(718 real changes made)

. replace turnout=. if Q23D==-1 | Q23D==0 | Q23D==9
(356 real changes made, 356 to missing)

. sum turnout

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     turnout |       844    .8507109    .3565845          0          1

. 
. keep ourprov DISTRICT ea gender age midage hhead celluse educ radio tv motor job interest turn
> out

. 
. replace ea=ea+1000
(1200 real changes made)

. 
. save moz_r4_data_r, replace
(note: file moz_r4_data_r.dta not found)
file moz_r4_data_r.dta saved

. 
. *R5
. 
. use moz_r5_data.dta, replace

. 
. *province
. 
. gen ourprov=0

. replace ourprov=1 if REGION==540 | REGION==542 | REGION==547 | REGION==549
(920 real changes made)

. tab REGION if ourprov==1

    Province or |
         region |      Freq.     Percent        Cum.
----------------+-----------------------------------
Maputo Province |        168       18.26       18.26
           Gaza |        136       14.78       33.04
       Zambezia |        432       46.96       80.00
   Cabo Delgado |        184       20.00      100.00
----------------+-----------------------------------
          Total |        920      100.00

. 
. *district
. 
. encode DISTRICT, gen(dist)

. tab dist

          District |      Freq.     Percent        Cum.
-------------------+-----------------------------------
     Alto Molocué |          8        0.33        0.33
           Ancuabe |         32        1.33        1.67
           Angoche |         24        1.00        2.67
           Angonia |         80        3.33        6.00
            Balama |         16        0.67        6.67
             Barue |         32        1.33        8.00
             Beira |         48        2.00       10.00
              Buzi |         32        1.33       11.33
              Caia |         16        0.67       12.00
          Changara |         16        0.67       12.67
            Chemba |         16        0.67       13.33
           Chibuto |          8        0.33       13.67
           Chimoio |         32        1.33       15.00
            Chinde |         24        1.00       16.00
            Chiure |          8        0.33       16.33
           Chiúta |         16        0.67       17.00
            Chokwe |         48        2.00       19.00
            Cuamba |         24        1.00       20.00
 Distrito Urbano 1 |         24        1.00       21.00
 Distrito Urbano 2 |         16        0.67       21.67
 Distrito Urbano 3 |         32        1.33       23.00
 Distrito Urbano 4 |         40        1.67       24.67
 Distrito Urbano 5 |         40        1.67       26.33
             Dondo |          8        0.33       26.67
              Gile |         16        0.67       27.33
           Gondola |         64        2.67       30.00
         Gorongosa |         24        1.00       31.00
             Guija |         16        0.67       31.67
             Gurue |         32        1.33       33.00
               Ile |         48        2.00       35.00
Ilha de Mocambique |         16        0.67       35.67
    Inhambane City |          8        0.33       36.00
         Inharrime |         56        2.33       38.33
         Inhassoro |         16        0.67       39.00
        Inhassunge |         16        0.67       39.67
              Lago |         32        1.33       41.00
          Lichinga |         24        1.00       42.00
            Lugela |         16        0.67       42.67
          Mabalane |         16        0.67       43.33
           Macomia |         40        1.67       45.00
  Maganja da Costa |         16        0.67       45.67
             Magoe |         16        0.67       46.33
            Majune |         16        0.67       47.00
            Malema |         40        1.67       48.67
          Mandimba |          8        0.33       49.00
        Mandlacaze |         24        1.00       50.00
           Manhica |         32        1.33       51.33
            Manica |          8        0.33       51.67
           Maravia |         24        1.00       52.67
          Maringue |         16        0.67       53.33
          Marromeu |          8        0.33       53.67
          Massinga |          8        0.33       54.00
       Matola City |         96        4.00       58.00
         Matutuine |         16        0.67       58.67
            Maxixe |          8        0.33       59.00
           Meconta |          8        0.33       59.33
          Mecuburi |         16        0.67       60.00
             Memba |         48        2.00       62.00
           Milange |         72        3.00       65.00
            Moamba |          8        0.33       65.33
           Moatize |         16        0.67       66.00
            Mocuba |         24        1.00       67.00
         Mogovolas |          8        0.33       67.33
              Moma |         56        2.33       69.67
            Monapo |         16        0.67       70.33
         Montepuez |         40        1.67       72.00
        Morrumbala |         32        1.33       73.33
        Morrumbene |         16        0.67       74.00
          Mossuril |         16        0.67       74.67
         Mossurize |         16        0.67       75.33
           Muecate |         48        2.00       77.33
             Mueda |         16        0.67       78.00
            Muembe |         32        1.33       79.33
          Mutarara |          8        0.33       79.67
      Nacala Porto |         24        1.00       80.67
      Nacala Velha |          8        0.33       81.00
           Nacaroa |         16        0.67       81.67
          Namaacha |         16        0.67       82.33
         Namacurra |         32        1.33       83.67
      Namapa-Erati |         40        1.67       85.33
           Nampula |         80        3.33       88.67
            Namuno |         16        0.67       89.33
            Ngauma |         16        0.67       90.00
        Nhamatanda |         16        0.67       90.67
         Nicoadala |         56        2.33       93.00
            Pebane |         16        0.67       93.67
      Pemba Cidade |         16        0.67       94.33
         Quelimane |         24        1.00       95.33
            Ribaue |          8        0.33       95.67
       Sussundenga |          8        0.33       96.00
       Tete Cidade |         24        1.00       97.00
          Tsangano |         16        0.67       97.67
         Vilankulo |         24        1.00       98.67
           Xai-Xai |         24        1.00       99.67
            Zavala |          8        0.33      100.00
-------------------+-----------------------------------
             Total |      2,400      100.00

. 
. *ea
. 
. egen ea_full=concat(dist EA_SVC_A EA_SVC_B EA_SVC_C EA_SVC_D EA_FAC_A EA_FAC_B EA_FAC_C EA_FAC
> _D EA_FAC_E EA_SEC_A EA_SEC_B EA_SEC_C EA_SEC_D EA_SEC_E EA_ROAD)

. encode ea_full, gen (ea)

. drop ea_full

. tab ea

               ea |      Freq.     Percent        Cum.
------------------+-----------------------------------
10000101111000000 |         16        0.67        0.67
11100101111100000 |         16        0.67        1.33
 1111111111100001 |          8        0.33        1.67
12000101000000000 |          8        0.33        2.00
13110101001000000 |         16        0.67        2.67
13110101011000000 |          8        0.33        3.00
13110101111000000 |          8        0.33        3.33
14000001111000000 |          8        0.33        3.67
14000100000000000 |          8        0.33        4.00
14000101000000000 |          8        0.33        4.33
15100101001000001 |          8        0.33        4.67
16000001110100000 |         16        0.67        5.33
17000109000000000 |         16        0.67        6.00
17110101011000000 |         16        0.67        6.67
17110101111100000 |          8        0.33        7.00
17110191111000001 |          8        0.33        7.33
18000001110000000 |         16        0.67        8.00
18100101000000000 |          8        0.33        8.33
19111101901000001 |          8        0.33        8.67
19111111111100001 |          8        0.33        9.00
19111111111110001 |          8        0.33        9.33
 2000001001000001 |          8        0.33        9.67
 2000101000000001 |          8        0.33       10.00
 2000101011000000 |         16        0.67       10.67
20110101911100000 |          8        0.33       11.00
20190100011000001 |          8        0.33       11.33
21110101000100000 |          8        0.33       11.67
21110101001100000 |          8        0.33       12.00
21110109911100001 |          8        0.33       12.33
21119101101100001 |          8        0.33       12.67
22110100000000000 |          8        0.33       13.00
22110101111000000 |          8        0.33       13.33
22111101111100000 |          8        0.33       13.67
22111101911000000 |          8        0.33       14.00
22119101101100001 |          8        0.33       14.33
23110101901000000 |          8        0.33       14.67
23110101991000000 |          8        0.33       15.00
23111101101100001 |          8        0.33       15.33
23111101111110000 |          8        0.33       15.67
23119101911000000 |          8        0.33       16.00
24110101011100000 |          8        0.33       16.33
25000101000000000 |         16        0.67       17.00
26000101001000000 |          8        0.33       17.33
26000101111100000 |         24        1.00       18.33
26100101111000000 |          8        0.33       18.67
26100101111100000 |         16        0.67       19.33
26100101111100001 |          8        0.33       19.67
27100101111000001 |          8        0.33       20.00
27100101111100001 |          8        0.33       20.33
27190101111100000 |          8        0.33       20.67
28000101119100000 |         16        0.67       21.33
29000001101000000 |          8        0.33       21.67
29000001111100000 |          8        0.33       22.00
29000101111000000 |          8        0.33       22.33
29110111111100000 |          8        0.33       22.67
30000100000000000 |          8        0.33       23.00
30000100001000000 |          8        0.33       23.33
30000101011000000 |          8        0.33       23.67
30000101111100000 |          8        0.33       24.00
 3000101111000000 |          8        0.33       24.33
 3000101111110000 |          8        0.33       24.67
30100101111100000 |         16        0.67       25.33
31000101001100000 |          8        0.33       25.67
31000101111110000 |          8        0.33       26.00
 3100101001000000 |          8        0.33       26.33
32110101011000000 |          8        0.33       26.67
33000101011000000 |         16        0.67       27.33
33100101011000000 |         16        0.67       28.00
33100101111100001 |          8        0.33       28.33
33110101111100001 |         16        0.67       29.00
34000100001000000 |         16        0.67       29.67
35100101111000000 |         16        0.67       30.33
36000101001000000 |          8        0.33       30.67
36000101011000000 |          8        0.33       31.00
36000101111110000 |          8        0.33       31.33
36100101110110000 |          8        0.33       31.67
37100101001000000 |          8        0.33       32.00
37110101001110000 |          8        0.33       32.33
37110101111110000 |          8        0.33       32.67
38000100000000000 |          8        0.33       33.00
38000101000000000 |          8        0.33       33.33
39000101111100000 |         16        0.67       34.00
 4000001001000000 |         16        0.67       34.67
 4000101001000000 |         32        1.33       36.00
 4000101111000000 |         16        0.67       36.67
40100101001000000 |         16        0.67       37.33
40100101111100000 |         16        0.67       38.00
40110101111000001 |          8        0.33       38.33
41000001011000000 |         16        0.67       39.00
 4100101111000000 |         16        0.67       39.67
42100101111110000 |         16        0.67       40.33
43100101111100001 |         16        0.67       41.00
44000001100000000 |         16        0.67       41.67
44000101001000000 |         16        0.67       42.33
44110101000000000 |          8        0.33       42.67
45100101100100000 |          8        0.33       43.00
46000101010000000 |          8        0.33       43.33
46100101111100000 |         16        0.67       44.00
47000109000000000 |         16        0.67       44.67
47100101010000001 |          8        0.33       45.00
47110101111100001 |          8        0.33       45.33
48110101101000000 |          8        0.33       45.67
49100101111100000 |         24        1.00       46.67
 5000001011000000 |          8        0.33       47.00
50000101111100000 |         16        0.67       47.67
 5000101000000000 |          8        0.33       48.00
51100101111100000 |          8        0.33       48.33
52000101991100000 |          8        0.33       48.67
53100101001000000 |          8        0.33       49.00
53100101101000000 |          8        0.33       49.33
53110100001010000 |          8        0.33       49.67
53110101000000000 |          8        0.33       50.00
53110101001000001 |          8        0.33       50.33
53110101009000000 |          8        0.33       50.67
53110101111100000 |         16        0.67       51.33
53111101991100001 |          8        0.33       51.67
53111111111100001 |         16        0.67       52.33
53119101101100000 |          8        0.33       52.67
54100101111100000 |         16        0.67       53.33
55110101911000000 |          8        0.33       53.67
56100101111100000 |          8        0.33       54.00
57000101111000000 |         16        0.67       54.67
58000100001000000 |          8        0.33       55.00
58000101011100000 |          8        0.33       55.33
58000101111000000 |         16        0.67       56.00
58000101111100000 |          8        0.33       56.33
58100101111100000 |          8        0.33       56.67
59000001110000000 |         24        1.00       57.67
59000001111000000 |          8        0.33       58.00
59100101111000000 |         32        1.33       59.33
59111111111110000 |          8        0.33       59.67
 6000100000000001 |          8        0.33       60.00
 6000101011000001 |          8        0.33       60.33
60111101111110000 |          8        0.33       60.67
61000101111000000 |         16        0.67       61.33
 6100100001000001 |          8        0.33       61.67
 6100101111000001 |          8        0.33       62.00
62100101001000000 |          8        0.33       62.33
62100101001000001 |          8        0.33       62.67
62100101011000000 |          8        0.33       63.00
63000101111110000 |          8        0.33       63.33
64000101001000000 |          8        0.33       63.67
64000101001000010 |          8        0.33       64.00
64100101010000000 |          8        0.33       64.33
64100101011000000 |          8        0.33       64.67
64100901001000000 |         16        0.67       65.33
64199101000000000 |          8        0.33       65.67
65000000000000000 |          8        0.33       66.00
65000001011000000 |          8        0.33       66.33
66000101111000001 |          8        0.33       66.67
66100101111000001 |          8        0.33       67.00
66100101111100001 |         16        0.67       67.67
66110101111110000 |          8        0.33       68.00
67100101111000000 |         16        0.67       68.67
67100101111100000 |         16        0.67       69.33
68110101111000000 |         16        0.67       70.00
69000100001000000 |          8        0.33       70.33
69100101111100000 |          8        0.33       70.67
70000100000000000 |          8        0.33       71.00
70000101111000000 |          8        0.33       71.33
71000001011000000 |         16        0.67       72.00
71000001111000000 |         16        0.67       72.67
71000001111100000 |         16        0.67       73.33
 7110101011000000 |          8        0.33       73.67
 7110101011000001 |          8        0.33       74.00
 7110101111000000 |          8        0.33       74.33
 7110101111100001 |         16        0.67       75.00
 7111111111110001 |          8        0.33       75.33
72000101001000000 |         16        0.67       76.00
73000101111000000 |         16        0.67       76.67
73000101111100000 |         16        0.67       77.33
74100101111100000 |          8        0.33       77.67
75100101001000000 |          8        0.33       78.00
75100101111000000 |          8        0.33       78.33
75100101111100000 |          8        0.33       78.67
76110101111100001 |          8        0.33       79.00
77000100001000000 |          8        0.33       79.33
77000101111000000 |          8        0.33       79.67
78100101119100000 |         16        0.67       80.33
79000001011000000 |          8        0.33       80.67
79000100001000000 |          8        0.33       81.00
79000101011000000 |         16        0.67       81.67
80000001111100000 |         16        0.67       82.33
80000101111100000 |          8        0.33       82.67
80000101111100001 |          8        0.33       83.00
80100101111100001 |          8        0.33       83.33
 8100101011000000 |         16        0.67       84.00
 8100101111100000 |         16        0.67       84.67
81100101001000001 |          8        0.33       85.00
81100101111100000 |         16        0.67       85.67
81100101111100001 |          8        0.33       86.00
81110101101100000 |          8        0.33       86.33
81110101111000001 |          8        0.33       86.67
81110101111100000 |          8        0.33       87.00
81110101111100001 |          8        0.33       87.33
81111100011100001 |          8        0.33       87.67
81111101101110011 |          8        0.33       88.00
82100101111000000 |         16        0.67       88.67
83000101111000000 |         16        0.67       89.33
84100101111100000 |         16        0.67       90.00
85000101011000000 |         16        0.67       90.67
85100101111100000 |         32        1.33       92.00
85110101111100000 |          8        0.33       92.33
86000001000000000 |         16        0.67       93.00
87111101111100001 |         16        0.67       93.67
88110101011000000 |          8        0.33       94.00
88110101011100000 |          8        0.33       94.33
88110101111000000 |          8        0.33       94.67
89100101001000000 |          8        0.33       95.00
90110101111100000 |          8        0.33       95.33
 9100101111000000 |          8        0.33       95.67
 9110101111110000 |          8        0.33       96.00
91111111111100001 |         24        1.00       97.00
92100101111100000 |         16        0.67       97.67
93000101011000000 |         16        0.67       98.33
93110191111100001 |          8        0.33       98.67
94000101010000000 |          8        0.33       99.00
94000101010000001 |          8        0.33       99.33
94110101111000000 |          8        0.33       99.67
95000109001000000 |          8        0.33      100.00
------------------+-----------------------------------
            Total |      2,400      100.00

. 
. *gender
. 
. gen gender=THISINT

. replace gender=0 if THISINT==2
(1199 real changes made)

. sum gender

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
      gender |      2400    .5004167     .500104          0          1

. 
. *age
. 
. gen age=Q1

. replace age=. if age==999
(137 real changes made, 137 to missing)

. sum age

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
         age |      2263    33.24746    12.59695         18         89

. gen midage=1

. replace midage=0 if age<30 | age>50
(1491 real changes made)

. replace midage=. if age==.
(137 real changes made, 137 to missing)

. sum midage

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
      midage |      2263    .4016792    .4903461          0          1

. 
. *hhead N/A
. 
. *celluse
. 
. capture drop celluse

. gen celluse=0

. replace celluse=1 if Q92>0
(1719 real changes made)

. replace celluse=. if Q92==9
(12 real changes made, 12 to missing)

. replace celluse=0 if celluse!=0 & Q93A==0 & Q93B==0 & Q93C==0
(22 real changes made)

. sum celluse

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     celluse |      2394    .7063492     .455529          0          1

. 
. *education (0-9)
. 
. gen educ=Q97

. replace educ=. if Q97==99 | Q97==998 | Q97==-1
(12 real changes made, 12 to missing)

. tab educ

       educ |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        286       11.98       11.98
          1 |         38        1.59       13.57
          2 |        623       26.09       39.66
          3 |        348       14.57       54.23
          4 |        566       23.70       77.93
          5 |        351       14.70       92.63
          6 |         81        3.39       96.02
          7 |         55        2.30       98.32
          8 |         40        1.68      100.00
------------+-----------------------------------
      Total |      2,388      100.00

. 
. *property
. 
. gen radio=Q90A

. replace radio=. if Q90A==9 | Q90A==998 | Q90A==-1
(1 real change made, 1 to missing)

. sum radio

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       radio |      2399    .6882034    .4633238          0          1

. 
. gen tv=Q90B

. replace tv=. if Q90B==9 | Q90B==998 | Q90B==-1
(4 real changes made, 4 to missing)

. sum tv

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
          tv |      2396    .3806344    .4856442          0          1

. 
. gen motor=Q90C

. replace motor=. if Q90C==9 | Q90C==998 | Q90C==-1
(17 real changes made, 17 to missing)

. sum motor

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       motor |      2383     .193034    .3947623          0          1

. 
. *job
. 
. gen job=.
(2400 missing values generated)

. replace job=1 if Q96==2 | Q96==3
(682 real changes made)

. replace job=0 if Q96==0 | Q96==1
(1682 real changes made)

. tab job

        job |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,682       71.15       71.15
          1 |        682       28.85      100.00
------------+-----------------------------------
      Total |      2,364      100.00

. 
. *interest (0-3)
. 
. gen interest=Q14

. replace interest=. if interest==9 | interest==-1
(137 real changes made, 137 to missing)

. tab interest

   interest |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        486       21.48       21.48
          1 |        365       16.13       37.60
          2 |        669       29.56       67.17
          3 |        743       32.83      100.00
------------+-----------------------------------
      Total |      2,263      100.00

. 
. *turnout09
. 
. gen turnout=0

. replace turnout=1 if Q27==1
(1681 real changes made)

. replace turnout=. if Q27==8 | Q27==0 | Q27==6 | Q27==9
(420 real changes made, 420 to missing)

. sum turnout

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     turnout |      1980    .8489899    .3581492          0          1

. 
. keep ourprov DISTRICT ea gender age midage celluse educ radio tv motor job interest turnout

. 
. gen hhead=.
(2400 missing values generated)

. 
. label drop ea

. 
. replace ea=ea+2000
(2400 real changes made)

. 
. save moz_r5_data_r, replace
(note: file moz_r5_data_r.dta not found)
file moz_r5_data_r.dta saved

. 
. *join datasets
. 
. use moz_r3_data_r, replace

. 
. numlabel ,add
(dataset has no value labels)

. 
. gen round=3

. 
. append using moz_r4_data_r
ea was long now double

. 
. replace round=4 if round==.
(1200 real changes made)

. 
. append using moz_r5_data_r

. 
. replace round=5 if round==.
(2400 real changes made)

. 
. replace DISTRICT=strupper(DISTRICT)
(2400 real changes made)

. 
. replace DISTRICT="ALTO MOLOCUE" if DISTRICT=="ALTO-MOLOCUE"
(16 real changes made)

. replace DISTRICT="ALTO MOLOCUE" if DISTRICT=="ALTO MOLOCUé"
(8 real changes made)

. replace DISTRICT="CHIUTA" if DISTRICT=="CHIúTA"
(16 real changes made)

. replace DISTRICT="DISTRITO URBANO 1" if DISTRICT=="D. URBANO N� 1"
(8 real changes made)

. replace DISTRICT="DISTRITO URBANO 4" if DISTRICT=="D. URBANO N� 4"
(32 real changes made)

. replace DISTRICT="DISTRITO URBANO 5" if DISTRICT=="D. URBANO N� 5"
(8 real changes made)

. replace DISTRICT="DISTRITO URBANO 2" if DISTRICT=="D.URB N�2"
(8 real changes made)

. replace DISTRICT="DISTRITO URBANO 3" if DISTRICT=="D.URB.N�3"
(24 real changes made)

. replace DISTRICT="DISTRITO URBANO 3" if DISTRICT=="DISTRITO URBANO N 3"
(8 real changes made)

. replace DISTRICT="DISTRITO URBANO 4" if DISTRICT=="DISTRITO URBANO N 4"
(16 real changes made)

. replace DISTRICT="DISTRITO URBANO 5" if DISTRICT=="DISTRITO URBANO N 5"
(16 real changes made)

. replace DISTRICT="ILHA DE MOCAMBIQUE" if DISTRICT=="ILHA DE MO�AMBIQUE"
(8 real changes made)

. replace DISTRICT="CIDADE DE INHAMBANE" if DISTRICT=="INHAMBANE CITY"
(8 real changes made)

. replace DISTRICT="MANHICA" if DISTRICT=="MANHI�A"
(16 real changes made)

. replace DISTRICT="CIDADE DA MATOLA" if DISTRICT=="MATOLA"
(32 real changes made)

. replace DISTRICT="CIDADE DA MATOLA" if DISTRICT=="MATOLA CIDADE"
(16 real changes made)

. replace DISTRICT="CIDADE DA MATOLA" if DISTRICT=="MATOLA CITY"
(96 real changes made)

. replace DISTRICT="CIDADE DA MAXIXE" if DISTRICT=="MAXIXE"
(16 real changes made)

. replace DISTRICT="MOCIMBOA DA PRAIA" if DISTRICT=="MOC�MBOA DA PRAIA"
(16 real changes made)

. replace DISTRICT="MOSSURIZE" if DISTRICT=="MUSSURIZE"
(8 real changes made)

. replace DISTRICT="NACALA PORTO" if DISTRICT=="NACALA - PORTO"
(8 real changes made)

. replace DISTRICT="CIDADE DE NAMPULA" if DISTRICT=="NAMPULA"
(80 real changes made)

. replace DISTRICT="CIDADE DE PEMBA" if DISTRICT=="PEMBA CIDADE"
(24 real changes made)

. replace DISTRICT="CIDADE DE QUELIMANE" if DISTRICT=="QUELIMANE"
(32 real changes made)

. replace DISTRICT="CIDADE DE TETE" if DISTRICT=="TETE"
(16 real changes made)

. replace DISTRICT="CIDADE DE TETE" if DISTRICT=="TETE CIDADE"
(24 real changes made)

. replace DISTRICT="VILANCULOS" if DISTRICT=="VILANKULO"
(32 real changes made)

. replace DISTRICT="CIDADE DE XAI-XAI" if DISTRICT=="XAI-XAI"
(40 real changes made)

. 
. encode DISTRICT, gen(dist)

. tab dist

           District |      Freq.     Percent        Cum.
--------------------+-----------------------------------
       ALTO MOLOCUE |         48        1.00        1.00
            ANCUABE |         56        1.17        2.17
            ANGOCHE |         64        1.33        3.50
            ANGONIA |        112        2.33        5.84
             BALAMA |         16        0.33        6.17
              BARUE |         40        0.83        7.00
              BEIRA |        104        2.17        9.17
             BILENE |         32        0.67        9.84
              BOANE |         16        0.33       10.17
               BUZI |         40        0.83       11.00
       CAHORA BASSA |         16        0.33       11.34
               CAIA |         48        1.00       12.34
           CHANGARA |         40        0.83       13.17
             CHEMBA |         16        0.33       13.51
          CHIBABAVA |          8        0.17       13.67
            CHIBUTO |         24        0.50       14.17
            CHIMOIO |         64        1.33       15.51
             CHINDE |         24        0.50       16.01
             CHIURE |         40        0.83       16.84
             CHIUTA |         40        0.83       17.67
             CHOKWE |         72        1.50       19.17
   CIDADE DA MATOLA |        160        3.33       22.51
   CIDADE DA MAXIXE |         32        0.67       23.18
CIDADE DE INHAMBANE |         24        0.50       23.68
  CIDADE DE NAMPULA |        136        2.83       26.51
    CIDADE DE PEMBA |         40        0.83       27.34
CIDADE DE QUELIMANE |         64        1.33       28.68
     CIDADE DE TETE |         56        1.17       29.85
  CIDADE DE XAI-XAI |         56        1.17       31.01
             CUAMBA |         63        1.31       32.33
  DISTRITO URBANO 1 |         32        0.67       32.99
  DISTRITO URBANO 2 |         40        0.83       33.83
  DISTRITO URBANO 3 |         64        1.33       35.16
  DISTRITO URBANO 4 |         88        1.83       36.99
  DISTRITO URBANO 5 |         64        1.33       38.33
              DONDO |         48        1.00       39.33
               GILE |         32        0.67       40.00
            GONDOLA |        104        2.17       42.16
          GORONGOSA |         32        0.67       42.83
              GUIJA |         40        0.83       43.66
               GURO |          8        0.17       43.83
              GURUE |         88        1.83       45.66
            HOMOINE |         24        0.50       46.17
                ILE |         80        1.67       47.83
 ILHA DE MOCAMBIQUE |         24        0.50       48.33
          INHARRIME |         56        1.17       49.50
          INHASSORO |         24        0.50       50.00
         INHASSUNGE |         16        0.33       50.33
            JANGAMO |         16        0.33       50.67
               LAGO |         32        0.67       51.33
           LICHINGA |         55        1.15       52.48
             LUGELA |         32        0.67       53.15
           MABALANE |         16        0.33       53.48
            MACANGA |         16        0.33       53.81
            MACHAZE |         16        0.33       54.15
            MACOMIA |         48        1.00       55.15
   MAGANJA DA COSTA |         32        0.67       55.81
              MAGOE |         16        0.33       56.15
             MAGUDE |          8        0.17       56.32
             MAJUNE |         16        0.33       56.65
             MALEMA |         64        1.33       57.98
             MAMUMO |          8        0.17       58.15
           MANDIMBA |         24        0.50       58.65
         MANDLACAZE |         32        0.67       59.32
            MANHICA |         64        1.33       60.65
             MANICA |         24        0.50       61.15
     MAPUTO-CATEMBE |          8        0.17       61.32
            MARAVIA |         24        0.50       61.82
           MARINGUE |         16        0.33       62.15
         MARRACUENE |          8        0.17       62.32
           MARROMEU |         40        0.83       63.15
           MASSINGA |         16        0.33       63.48
          MASSINGIR |         16        0.33       63.82
          MATUTUINE |         16        0.33       64.15
         MECANHELAS |         16        0.33       64.49
            MECONTA |         24        0.50       64.99
           MECUBURI |         48        1.00       65.99
              MEMBA |         80        1.67       67.65
           METARICA |         16        0.33       67.99
            MILANGE |        136        2.83       70.82
             MOAMBA |         16        0.33       71.15
            MOATIZE |         48        1.00       72.16
  MOCIMBOA DA PRAIA |         24        0.50       72.66
             MOCUBA |         88        1.83       74.49
          MOGOVOLAS |         40        0.83       75.32
               MOMA |         88        1.83       77.16
             MONAPO |         40        0.83       77.99
          MONTEPUEZ |         72        1.50       79.49
         MORRUMBALA |         72        1.50       80.99
         MORRUMBENE |         40        0.83       81.83
           MOSSURIL |         16        0.33       82.16
          MOSSURIZE |         40        0.83       82.99
            MUECATE |         64        1.33       84.33
              MUEDA |         24        0.50       84.83
             MUEMBE |         32        0.67       85.49
           MUIDUMBE |          8        0.17       85.66
          MURRUPULA |          8        0.17       85.83
           MUTARARA |         32        0.67       86.49
       NACALA PORTO |         56        1.17       87.66
       NACALA VELHA |          8        0.17       87.83
            NACAROA |         32        0.67       88.50
           NAMAACHA |         16        0.33       88.83
          NAMACURRA |         56        1.17       90.00
             NAMAPA |         24        0.50       90.50
       NAMAPA-ERATI |         64        1.33       91.83
           NAMARROI |         16        0.33       92.16
             NAMUNO |         32        0.67       92.83
             NGAUMA |         16        0.33       93.16
         NHAMATANDA |         40        0.83       94.00
          NICOADALA |         72        1.50       95.50
              PALMA |          8        0.17       95.66
             PEBANE |         32        0.67       96.33
             RAPALE |         24        0.50       96.83
             RIBAUE |         24        0.50       97.33
              SANGA |          8        0.17       97.50
        SUSSUNDENGA |         24        0.50       98.00
           TSANGANO |         16        0.33       98.33
         VILANCULOS |         48        1.00       99.33
             ZAVALA |         32        0.67      100.00
--------------------+-----------------------------------
              Total |      4,798      100.00

. 
. *regs
. 
. gen hheadcelluse=hhead*celluse
(3613 missing values generated)

. 
. global out="turnout"

. 
. global list1=""

. global list2=""

. 
. foreach i in $out {
  2. 
.         reg `i' hhead, cluster(dist)
  3.         estimates store `i'_2_1
  4.         sum `i' if e(sample) == 1
  5.         scalar define m_`i'_2_1=r(mean)
  6.         reg `i' celluse, cluster(dist)
  7.         estimates store `i'_3_1
  8.         sum `i' if e(sample) == 1
  9.         scalar define m_`i'_3_1=r(mean)
 10.         reg `i' hheadcelluse, cluster(dist)
 11.         estimates store `i'_4_1
 12.         sum `i' if e(sample) == 1
 13.         scalar define m_`i'_4_1=r(mean)
 14. 
.         xi: reg `i' hhead gender i.educ radio tv motor job i.round i.dist, cluster(dist)
 15.         estimates store `i'_2_2
 16.         sum `i' if e(sample) == 1
 17.         scalar define m_`i'_2_2=r(mean)
 18.         xi: reg `i' hhead gender i.educ radio tv motor job i.round i.dist if ourprov==1, cl
> uster(dist)
 19.         estimates store `i'_2_3
 20.         sum `i' if e(sample) == 1
 21.         scalar define m_`i'_2_3=r(mean)
 22. 
.         xi: reg `i' celluse gender i.educ radio tv motor job i.round i.dist, cluster(dist)
 23.         estimates store `i'_3_2
 24.         sum `i' if e(sample) == 1
 25.         scalar define m_`i'_3_2=r(mean)
 26.         xi: reg `i' celluse gender i.educ radio tv motor job i.round i.dist if ourprov==1, 
> cluster(dist)
 27.         estimates store `i'_3_3
 28.         sum `i' if e(sample) == 1
 29.         scalar define m_`i'_3_3=r(mean)
 30. 
.         xi: reg `i' hheadcelluse gender i.educ radio tv motor job i.round i.dist, cluster(dist
> )
 31.         estimates store `i'_4_2
 32.         sum `i' if e(sample) == 1
 33.         scalar define m_`i'_4_2=r(mean)
 34.         xi: reg `i' hheadcelluse gender i.educ radio tv motor job i.round i.dist if ourprov
> ==1, cluster(dist)
 35.         estimates store `i'_4_3
 36.         sum `i' if e(sample) == 1
 37.         scalar define m_`i'_4_3=r(mean)
 38. 
.         global list1="$list1" +" `i'_2_1" + " `i'_3_1" + " `i'_4_1" + " `i'_2_2" + " `i'_2_3"+
>  " `i'_3_2" + " `i'_3_3" + " `i'_4_2" + " `i'_4_3"        
 39. 
.         }

Linear regression                                      Number of obs =    1970
                                                       F(  1,   100) =   36.10
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0279
                                                       Root MSE      =  .37276

                                 (Std. Err. adjusted for 101 clusters in dist)
------------------------------------------------------------------------------
             |               Robust
     turnout |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       hhead |   .1299107   .0216224     6.01   0.000     .0870124     .172809
       _cons |   .7473545   .0206738    36.15   0.000     .7063383    .7883707
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     turnout |      1970    .8274112    .3779874          0          1

Linear regression                                      Number of obs =    2817
                                                       F(  1,   106) =    0.08
                                                       Prob > F      =  0.7823
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .35735

                                 (Std. Err. adjusted for 107 clusters in dist)
------------------------------------------------------------------------------
             |               Robust
     turnout |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     celluse |   .0042691   .0154125     0.28   0.782    -.0262876    .0348258
       _cons |   .8471276   .0121238    69.87   0.000     .8230909    .8711642
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     turnout |      2817    .8498403    .3572914          0          1

Linear regression                                      Number of obs =     831
                                                       F(  1,    74) =    7.82
                                                       Prob > F      =  0.0066
                                                       R-squared     =  0.0094
                                                       Root MSE      =  .35505

                                  (Std. Err. adjusted for 75 clusters in dist)
------------------------------------------------------------------------------
             |               Robust
     turnout |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hheadcelluse |   .0754358   .0269708     2.80   0.007     .0216952    .1291763
       _cons |   .8281787   .0220351    37.58   0.000     .7842728    .8720846
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     turnout |       831    .8507822    .3565176          0          1
i.educ            _Ieduc_0-9          (naturally coded; _Ieduc_0 omitted)
i.round           _Iround_3-5         (naturally coded; _Iround_3 omitted)
i.dist            _Idist_1-119        (naturally coded; _Idist_1 omitted)
note: _Iround_5 omitted because of collinearity
note: _Idist_5 omitted because of collinearity
note: _Idist_14 omitted because of collinearity
note: _Idist_18 omitted because of collinearity
note: _Idist_46 omitted because of collinearity
note: _Idist_48 omitted because of collinearity
note: _Idist_50 omitted because of collinearity
note: _Idist_53 omitted because of collinearity
note: _Idist_58 omitted because of collinearity
note: _Idist_60 omitted because of collinearity
note: _Idist_68 omitted because of collinearity
note: _Idist_69 omitted because of collinearity
note: _Idist_74 omitted because of collinearity
note: _Idist_91 omitted because of collinearity
note: _Idist_95 omitted because of collinearity
note: _Idist_100 omitted because of collinearity
note: _Idist_102 omitted because of collinearity
note: _Idist_108 omitted because of collinearity
note: _Idist_117 omitted because of collinearity

Linear regression                                      Number of obs =    1900
                                                       F( 14,   100) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.1193
                                                       Root MSE      =  .36434

                                 (Std. Err. adjusted for 101 clusters in dist)
------------------------------------------------------------------------------
             |               Robust
     turnout |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       hhead |   .1235713   .0246966     5.00   0.000     .0745739    .1725687
      gender |    -.00826    .018554    -0.45   0.657    -.0450706    .0285507
    _Ieduc_1 |   .0659878   .0453169     1.46   0.148    -.0239197    .1558953
    _Ieduc_2 |   .0164696   .0289486     0.57   0.571    -.0409636    .0739028
    _Ieduc_3 |  -.0240574   .0369139    -0.65   0.516    -.0972936    .0491788
    _Ieduc_4 |   .0465798   .0337307     1.38   0.170    -.0203411    .1135006
    _Ieduc_5 |  -.0071423   .0441217    -0.16   0.872    -.0946785    .0803938
    _Ieduc_6 |  -.0587154   .0756933    -0.78   0.440    -.2088887    .0914578
    _Ieduc_7 |   .0716272   .0958127     0.75   0.456    -.1184624    .2617169
    _Ieduc_8 |  -.0383083   .1698908    -0.23   0.822    -.3753667    .2987502
    _Ieduc_9 |  -.6955202    .029754   -23.38   0.000    -.7545512   -.6364891
       radio |   .0389882   .0200327     1.95   0.054    -.0007562    .0787325
          tv |   .0436862   .0234576     1.86   0.065    -.0028529    .0902254
       motor |  -.0148886   .0319719    -0.47   0.642    -.0783198    .0485427
         job |   .0270159   .0190447     1.42   0.159    -.0107684    .0648001
   _Iround_4 |   .0195492   .0223738     0.87   0.384    -.0248397    .0639382
   _Iround_5 |          0  (omitted)
    _Idist_2 |   .0917814   .0096167     9.54   0.000     .0727022    .1108606
    _Idist_3 |   .1920592   .0189232    10.15   0.000     .1545161    .2296022
    _Idist_4 |  -.0499094   .0104409    -4.78   0.000    -.0706238    -.029195
    _Idist_5 |          0  (omitted)
    _Idist_6 |   -.003839   .0166059    -0.23   0.818    -.0367847    .0291066
    _Idist_7 |  -.0005619   .0188082    -0.03   0.976     -.037877    .0367531
    _Idist_8 |   .0876604   .0128774     6.81   0.000     .0621121    .1132087
    _Idist_9 |    .176589   .0189545     9.32   0.000     .1389839    .2141941
   _Idist_10 |   .2136103   .0165904    12.88   0.000     .1806954    .2465253
   _Idist_11 |  -.1686407   .0225123    -7.49   0.000    -.2133044    -.123977
   _Idist_12 |  -.0151294   .0129147    -1.17   0.244    -.0407517    .0104929
   _Idist_13 |   .1610088    .010599    15.19   0.000     .1399806     .182037
   _Idist_14 |          0  (omitted)
   _Idist_15 |   .2436681   .0198987    12.25   0.000     .2041897    .2831465
   _Idist_16 |   .1482131   .0165813     8.94   0.000     .1153162      .18111
   _Idist_17 |  -.1222075   .0106325   -11.49   0.000    -.1433021   -.1011129
   _Idist_18 |          0  (omitted)
   _Idist_19 |   .0746532   .0124531     5.99   0.000     .0499467    .0993597
   _Idist_20 |   .1106702    .011445     9.67   0.000     .0879636    .1333767
   _Idist_21 |   .1571286   .0156314    10.05   0.000     .1261164    .1881408
   _Idist_22 |   .0453783   .0112698     4.03   0.000     .0230194    .0677371
   _Idist_23 |   .0426517   .0145309     2.94   0.004     .0138228    .0714806
   _Idist_24 |   .0344456   .0173653     1.98   0.050    -6.61e-06    .0688978
   _Idist_25 |  -.0513836   .0126775    -4.05   0.000    -.0765353   -.0262319
   _Idist_26 |  -.0135953   .0158427    -0.86   0.393    -.0450268    .0178363
   _Idist_27 |    .078089   .0143182     5.45   0.000      .049682     .106496
   _Idist_28 |  -.0516197   .0117757    -4.38   0.000    -.0749823    -.028257
   _Idist_29 |  -.0621941   .0203597    -3.05   0.003    -.1025871   -.0218011
   _Idist_30 |   .1558599   .0112856    13.81   0.000     .1334696    .1782503
   _Idist_31 |  -.0870132   .0397496    -2.19   0.031    -.1658753   -.0081512
   _Idist_32 |   .0696669    .017835     3.91   0.000     .0342827    .1050511
   _Idist_33 |  -.0056636   .0150788    -0.38   0.708    -.0355795    .0242522
   _Idist_34 |   .1260952   .0140613     8.97   0.000      .098198    .1539923
   _Idist_35 |   .0637075   .0299835     2.12   0.036     .0042211     .123194
   _Idist_36 |  -.0582127   .0109281    -5.33   0.000    -.0798938   -.0365316
   _Idist_37 |   .1128366   .0156012     7.23   0.000     .0818844    .1437889
   _Idist_38 |   .1282534   .0088818    14.44   0.000     .1106321    .1458746
   _Idist_39 |   -.026864   .0219235    -1.23   0.223    -.0703595    .0166316
   _Idist_40 |  -.0310275   .0132627    -2.34   0.021    -.0573403   -.0047147
   _Idist_41 |   .2245488   .0196101    11.45   0.000     .1856429    .2634548
   _Idist_42 |   .0485848   .0054994     8.83   0.000     .0376741    .0594955
   _Idist_43 |   .1106874   .0141607     7.82   0.000     .0825931    .1387818
   _Idist_44 |   .1891278    .009169    20.63   0.000     .1709367    .2073189
   _Idist_45 |   .2741181   .0239161    11.46   0.000     .2266693    .3215669
   _Idist_46 |          0  (omitted)
   _Idist_47 |   .2321879   .0197359    11.76   0.000     .1930325    .2713433
   _Idist_48 |          0  (omitted)
   _Idist_49 |   .1035086   .0157041     6.59   0.000     .0723521     .134665
   _Idist_50 |          0  (omitted)
   _Idist_51 |   .1641237   .0142132    11.55   0.000     .1359252    .1923223
   _Idist_52 |   .0302545   .0192072     1.58   0.118    -.0078522    .0683611
   _Idist_53 |          0  (omitted)
   _Idist_54 |   .0192131   .0200042     0.96   0.339    -.0204746    .0589008
   _Idist_55 |   .2698516   .0216639    12.46   0.000     .2268711    .3128322
   _Idist_56 |   .1863432   .0214979     8.67   0.000      .143692    .2289945
   _Idist_57 |  -.2259843   .0228308    -9.90   0.000      -.27128   -.1806886
   _Idist_58 |          0  (omitted)
   _Idist_59 |  -.0226496   .0242996    -0.93   0.354    -.0708593    .0255601
   _Idist_60 |          0  (omitted)
   _Idist_61 |   .1021505   .0087194    11.72   0.000     .0848514    .1194496
   _Idist_62 |  -.0157856   .0171126    -0.92   0.359    -.0497366    .0181653
   _Idist_63 |   .1981462    .020227     9.80   0.000     .1580165     .238276
   _Idist_64 |   .2058889   .0261909     7.86   0.000     .1539269    .2578509
   _Idist_65 |   .0296662   .0153087     1.94   0.055    -.0007058    .0600382
   _Idist_66 |    .041528   .0143134     2.90   0.005     .0131307    .0699254
   _Idist_67 |   .1533548    .019035     8.06   0.000     .1155899    .1911196
   _Idist_68 |          0  (omitted)
   _Idist_69 |          0  (omitted)
   _Idist_70 |  -.0058331   .0219309    -0.27   0.791    -.0493434    .0376772
   _Idist_71 |   .0023187   .0089601     0.26   0.796     -.015458    .0200954
   _Idist_72 |  -.2993221   .0235798   -12.69   0.000    -.3461039   -.2525404
   _Idist_73 |   .0931046   .0242356     3.84   0.000     .0450218    .1411874
   _Idist_74 |          0  (omitted)
   _Idist_75 |  -.0146151   .0212239    -0.69   0.493    -.0567227    .0274925
   _Idist_76 |   .1350991   .0114579    11.79   0.000      .112367    .1578312
   _Idist_77 |   .2336478   .0145426    16.07   0.000     .2047957       .2625
   _Idist_78 |   .1774173   .0127692    13.89   0.000     .1520836     .202751
   _Idist_79 |   .0891998   .0167667     5.32   0.000     .0559353    .1224644
   _Idist_80 |  -.1778859   .0105929   -16.79   0.000    -.1989019   -.1568699
   _Idist_81 |  -.0117868   .0244274    -0.48   0.630    -.0602501    .0366764
   _Idist_82 |  -.0749204   .0062491   -11.99   0.000    -.0873184   -.0625224
   _Idist_83 |  -.0113336   .0102009    -1.11   0.269    -.0315719    .0089046
   _Idist_84 |   .0369286   .0117185     3.15   0.002     .0136795    .0601777
   _Idist_85 |   .1855976   .0163575    11.35   0.000     .1531447    .2180505
   _Idist_86 |  -.0351175   .0152459    -2.30   0.023     -.065365     -.00487
   _Idist_87 |   .2204821   .0155777    14.15   0.000     .1895763    .2513879
   _Idist_88 |   .1171012   .0109946    10.65   0.000     .0952883    .1389142
   _Idist_89 |  -.0495722    .011332    -4.37   0.000    -.0720545   -.0270898
   _Idist_90 |  -.1234415   .0135643    -9.10   0.000    -.1503527   -.0965302
   _Idist_91 |          0  (omitted)
   _Idist_92 |  -.0481576   .0168779    -2.85   0.005    -.0816429   -.0146724
   _Idist_93 |   .0339213   .0202201     1.68   0.097    -.0061949    .0740374
   _Idist_94 |  -.0451117    .022184    -2.03   0.045    -.0891242   -.0010992
   _Idist_95 |          0  (omitted)
   _Idist_96 |   .2675904   .0204631    13.08   0.000     .2269922    .3081886
   _Idist_97 |   .0660978   .0217494     3.04   0.003     .0229477    .1092479
   _Idist_98 |  -.0679887   .0096546    -7.04   0.000    -.0871431   -.0488344
   _Idist_99 |   .1045423   .0149361     7.00   0.000     .0749095    .1341751
  _Idist_100 |          0  (omitted)
  _Idist_101 |   .1919169   .0143052    13.42   0.000     .1635359     .220298
  _Idist_102 |          0  (omitted)
  _Idist_103 |  -.0002203   .0079332    -0.03   0.978    -.0159595     .015519
  _Idist_104 |   .1207775   .0195691     6.17   0.000      .081953    .1596021
  _Idist_105 |   .0991457   .0142081     6.98   0.000     .0709572    .1273341
  _Idist_106 |   .0976327   .0166796     5.85   0.000     .0645409    .1307245
  _Idist_107 |   .1513467   .0168067     9.01   0.000     .1180026    .1846907
  _Idist_108 |          0  (omitted)
  _Idist_109 |   .1007027   .0089692    11.23   0.000     .0829081    .1184974
  _Idist_110 |   .1826999    .014052    13.00   0.000     .1548211    .2105787
  _Idist_111 |   .2035871   .0200168    10.17   0.000     .1638743    .2432999
  _Idist_112 |  -.0901103   .0155884    -5.78   0.000    -.1210373   -.0591834
  _Idist_113 |   .1483209   .0168585     8.80   0.000      .114874    .1817678
  _Idist_114 |   .1042691   .0132764     7.85   0.000      .077929    .1306091
  _Idist_115 |   .0625225   .0210488     2.97   0.004     .0207622    .1042828
  _Idist_116 |   .0142323   .0165934     0.86   0.393    -.0186887    .0471532
  _Idist_117 |          0  (omitted)
  _Idist_118 |  -.1675731   .0174802    -9.59   0.000    -.2022533    -.132893
  _Idist_119 |  -.1210911   .0149127    -8.12   0.000    -.1506775   -.0915047
       _cons |    .649563   .0365675    17.76   0.000      .577014    .7221119
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     turnout |      1900    .8294737    .3761935          0          1
i.educ            _Ieduc_0-9          (naturally coded; _Ieduc_0 omitted)
i.round           _Iround_3-5         (naturally coded; _Iround_3 omitted)
i.dist            _Idist_1-119        (naturally coded; _Idist_1 omitted)
note: _Iround_5 omitted because of collinearity
note: _Idist_3 omitted because of collinearity
note: _Idist_4 omitted because of collinearity
note: _Idist_5 omitted because of collinearity
note: _Idist_6 omitted because of collinearity
note: _Idist_7 omitted because of collinearity
note: _Idist_10 omitted because of collinearity
note: _Idist_11 omitted because of collinearity
note: _Idist_12 omitted because of collinearity
note: _Idist_13 omitted because of collinearity
note: _Idist_14 omitted because of collinearity
note: _Idist_15 omitted because of collinearity
note: _Idist_17 omitted because of collinearity
note: _Idist_18 omitted because of collinearity
note: _Idist_20 omitted because of collinearity
note: _Idist_23 omitted because of collinearity
note: _Idist_24 omitted because of collinearity
note: _Idist_25 omitted because of collinearity
note: _Idist_28 omitted because of collinearity
note: _Idist_30 omitted because of collinearity
note: _Idist_31 omitted because of collinearity
note: _Idist_32 omitted because of collinearity
note: _Idist_33 omitted because of collinearity
note: _Idist_34 omitted because of collinearity
note: _Idist_35 omitted because of collinearity
note: _Idist_36 omitted because of collinearity
note: _Idist_38 omitted because of collinearity
note: _Idist_39 omitted because of collinearity
note: _Idist_41 omitted because of collinearity
note: _Idist_43 omitted because of collinearity
note: _Idist_45 omitted because of collinearity
note: _Idist_46 omitted because of collinearity
note: _Idist_47 omitted because of collinearity
note: _Idist_48 omitted because of collinearity
note: _Idist_49 omitted because of collinearity
note: _Idist_50 omitted because of collinearity
note: _Idist_51 omitted because of collinearity
note: _Idist_53 omitted because of collinearity
note: _Idist_54 omitted because of collinearity
note: _Idist_55 omitted because of collinearity
note: _Idist_58 omitted because of collinearity
note: _Idist_60 omitted because of collinearity
note: _Idist_61 omitted because of collinearity
note: _Idist_63 omitted because of collinearity
note: _Idist_66 omitted because of collinearity
note: _Idist_67 omitted because of collinearity
note: _Idist_68 omitted because of collinearity
note: _Idist_69 omitted because of collinearity
note: _Idist_71 omitted because of collinearity
note: _Idist_72 omitted because of collinearity
note: _Idist_74 omitted because of collinearity
note: _Idist_75 omitted because of collinearity
note: _Idist_76 omitted because of collinearity
note: _Idist_77 omitted because of collinearity
note: _Idist_78 omitted because of collinearity
note: _Idist_79 omitted because of collinearity
note: _Idist_82 omitted because of collinearity
note: _Idist_85 omitted because of collinearity
note: _Idist_86 omitted because of collinearity
note: _Idist_87 omitted because of collinearity
note: _Idist_90 omitted because of collinearity
note: _Idist_91 omitted because of collinearity
note: _Idist_92 omitted because of collinearity
note: _Idist_93 omitted because of collinearity
note: _Idist_95 omitted because of collinearity
note: _Idist_97 omitted because of collinearity
note: _Idist_98 omitted because of collinearity
note: _Idist_99 omitted because of collinearity
note: _Idist_100 omitted because of collinearity
note: _Idist_101 omitted because of collinearity
note: _Idist_102 omitted because of collinearity
note: _Idist_104 omitted because of collinearity
note: _Idist_105 omitted because of collinearity
note: _Idist_108 omitted because of collinearity
note: _Idist_109 omitted because of collinearity
note: _Idist_113 omitted because of collinearity
note: _Idist_114 omitted because of collinearity
note: _Idist_115 omitted because of collinearity
note: _Idist_116 omitted because of collinearity
note: _Idist_117 omitted because of collinearity
note: _Idist_118 omitted because of collinearity
note: _Idist_119 omitted because of collinearity

Linear regression                                      Number of obs =     776
                                                       F( 13,    37) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.1303
                                                       Root MSE      =  .36968

                                  (Std. Err. adjusted for 38 clusters in dist)
------------------------------------------------------------------------------
             |               Robust
     turnout |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       hhead |   .1210362   .0385288     3.14   0.003     .0429694     .199103
      gender |  -.0251796    .029241    -0.86   0.395    -.0844275    .0340682
    _Ieduc_1 |    .111346    .074894     1.49   0.146    -.0404037    .2630957
    _Ieduc_2 |   .1012519   .0502789     2.01   0.051    -.0006229    .2031266
    _Ieduc_3 |   .0201919   .0671023     0.30   0.765    -.1157702    .1561541
    _Ieduc_4 |   .1310818   .0520384     2.52   0.016     .0256419    .2365217
    _Ieduc_5 |   .0393214   .0805859     0.49   0.628    -.1239611    .2026039
    _Ieduc_6 |  -.0298143     .10361    -0.29   0.775     -.239748    .1801194
    _Ieduc_7 |    .022072   .1290409     0.17   0.865    -.2393898    .2835338
    _Ieduc_8 |  -.9724139   .0798363   -12.18   0.000    -1.134178   -.8106502
    _Ieduc_9 |  -.6758203    .053285   -12.68   0.000     -.783786   -.5678545
       radio |   .0460332   .0311298     1.48   0.148    -.0170418    .1091083
          tv |  -.0073638   .0409799    -0.18   0.858     -.090397    .0756694
       motor |   .0702719   .0407806     1.72   0.093    -.0123575    .1529012
         job |   .0400119   .0289846     1.38   0.176    -.0187165    .0987403
   _Iround_4 |    .018149   .0425239     0.43   0.672    -.0680126    .1043106
   _Iround_5 |          0  (omitted)
    _Idist_2 |   .0927655   .0169813     5.46   0.000     .0583581    .1271728
    _Idist_3 |          0  (omitted)
    _Idist_4 |          0  (omitted)
    _Idist_5 |          0  (omitted)
    _Idist_6 |          0  (omitted)
    _Idist_7 |          0  (omitted)
    _Idist_8 |   .0975878    .018453     5.29   0.000     .0601984    .1349771
    _Idist_9 |   .2323512   .0312712     7.43   0.000     .1689897    .2957128
   _Idist_10 |          0  (omitted)
   _Idist_11 |          0  (omitted)
   _Idist_12 |          0  (omitted)
   _Idist_13 |          0  (omitted)
   _Idist_14 |          0  (omitted)
   _Idist_15 |          0  (omitted)
   _Idist_16 |   .1684532   .0251053     6.71   0.000      .117585    .2193214
   _Idist_17 |          0  (omitted)
   _Idist_18 |          0  (omitted)
   _Idist_19 |   .0819385   .0230916     3.55   0.001     .0351504    .1287266
   _Idist_20 |          0  (omitted)
   _Idist_21 |   .1804884   .0245719     7.35   0.000     .1307009    .2302759
   _Idist_22 |   .0703899   .0180635     3.90   0.000     .0337899    .1069899
   _Idist_23 |          0  (omitted)
   _Idist_24 |          0  (omitted)
   _Idist_25 |          0  (omitted)
   _Idist_26 |   .0080222   .0239234     0.34   0.739    -.0404512    .0564955
   _Idist_27 |   .1278276   .0229561     5.57   0.000     .0813141     .174341
   _Idist_28 |          0  (omitted)
   _Idist_29 |  -.0194297   .0362512    -0.54   0.595    -.0928816    .0540221
   _Idist_30 |          0  (omitted)
   _Idist_31 |          0  (omitted)
   _Idist_32 |          0  (omitted)
   _Idist_33 |          0  (omitted)
   _Idist_34 |          0  (omitted)
   _Idist_35 |          0  (omitted)
   _Idist_36 |          0  (omitted)
   _Idist_37 |   .0998437   .0263769     3.79   0.001      .046399    .1532884
   _Idist_38 |          0  (omitted)
   _Idist_39 |          0  (omitted)
   _Idist_40 |   -.022088   .0221992    -0.99   0.326    -.0670679    .0228919
   _Idist_41 |          0  (omitted)
   _Idist_42 |   .0574362   .0089031     6.45   0.000     .0393967    .0754757
   _Idist_43 |          0  (omitted)
   _Idist_44 |   .1973256   .0134047    14.72   0.000     .1701651    .2244861
   _Idist_45 |          0  (omitted)
   _Idist_46 |          0  (omitted)
   _Idist_47 |          0  (omitted)
   _Idist_48 |          0  (omitted)
   _Idist_49 |          0  (omitted)
   _Idist_50 |          0  (omitted)
   _Idist_51 |          0  (omitted)
   _Idist_52 |   .0402948   .0344706     1.17   0.250    -.0295493    .1101388
   _Idist_53 |          0  (omitted)
   _Idist_54 |          0  (omitted)
   _Idist_55 |          0  (omitted)
   _Idist_56 |   .2133477   .0342707     6.23   0.000     .1439086    .2827867
   _Idist_57 |  -.1942622   .0411089    -4.73   0.000    -.2775568   -.1109677
   _Idist_58 |          0  (omitted)
   _Idist_59 |  -.0064288   .0437334    -0.15   0.884    -.0950412    .0821836
   _Idist_60 |          0  (omitted)
   _Idist_61 |          0  (omitted)
   _Idist_62 |  -.0136253   .0307841    -0.44   0.661    -.0759998    .0487492
   _Idist_63 |          0  (omitted)
   _Idist_64 |   .2487504   .0453807     5.48   0.000     .1568005    .3407004
   _Idist_65 |   .0608648    .023282     2.61   0.013      .013691    .1080387
   _Idist_66 |          0  (omitted)
   _Idist_67 |          0  (omitted)
   _Idist_68 |          0  (omitted)
   _Idist_69 |          0  (omitted)
   _Idist_70 |   .0059269   .0308834     0.19   0.849    -.0566487    .0685026
   _Idist_71 |          0  (omitted)
   _Idist_72 |          0  (omitted)
   _Idist_73 |   .1386142   .0421266     3.29   0.002     .0532576    .2239709
   _Idist_74 |          0  (omitted)
   _Idist_75 |          0  (omitted)
   _Idist_76 |          0  (omitted)
   _Idist_77 |          0  (omitted)
   _Idist_78 |          0  (omitted)
   _Idist_79 |          0  (omitted)
   _Idist_80 |  -.1634663   .0179563    -9.10   0.000    -.1998492   -.1270833
   _Idist_81 |   .0333273   .0442437     0.75   0.456     -.056319    .1229735
   _Idist_82 |          0  (omitted)
   _Idist_83 |   -.011403   .0188371    -0.61   0.549    -.0495706    .0267645
   _Idist_84 |   .0479217   .0178899     2.68   0.011     .0116733    .0841701
   _Idist_85 |          0  (omitted)
   _Idist_86 |          0  (omitted)
   _Idist_87 |          0  (omitted)
   _Idist_88 |   .1259441   .0164031     7.68   0.000     .0927082      .15918
   _Idist_89 |    -.03511   .0184646    -1.90   0.065    -.0725229    .0023029
   _Idist_90 |          0  (omitted)
   _Idist_91 |          0  (omitted)
   _Idist_92 |          0  (omitted)
   _Idist_93 |          0  (omitted)
   _Idist_94 |  -.0027645   .0331811    -0.08   0.934    -.0699959    .0644669
   _Idist_95 |          0  (omitted)
   _Idist_96 |   .2810293   .0392164     7.17   0.000     .2015693    .3604893
   _Idist_97 |          0  (omitted)
   _Idist_98 |          0  (omitted)
   _Idist_99 |          0  (omitted)
  _Idist_100 |          0  (omitted)
  _Idist_101 |          0  (omitted)
  _Idist_102 |          0  (omitted)
  _Idist_103 |   .0084732   .0138718     0.61   0.545    -.0196338    .0365802
  _Idist_104 |          0  (omitted)
  _Idist_105 |          0  (omitted)
  _Idist_106 |   .0911212   .0279479     3.26   0.002     .0344934     .147749
  _Idist_107 |   .1488615   .0291423     5.11   0.000     .0898135    .2079095
  _Idist_108 |          0  (omitted)
  _Idist_109 |          0  (omitted)
  _Idist_110 |   .1873051   .0247329     7.57   0.000     .1371914    .2374188
  _Idist_111 |   .2406989   .0305209     7.89   0.000     .1788576    .3025402
  _Idist_112 |  -.0570181   .0240407    -2.37   0.023    -.1057292    -.008307
  _Idist_113 |          0  (omitted)
  _Idist_114 |          0  (omitted)
  _Idist_115 |          0  (omitted)
  _Idist_116 |          0  (omitted)
  _Idist_117 |          0  (omitted)
  _Idist_118 |          0  (omitted)
  _Idist_119 |          0  (omitted)
       _cons |   .5816275   .0673819     8.63   0.000     .4450988    .7181563
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     turnout |       776    .8221649    .3826204          0          1
i.educ            _Ieduc_0-9          (naturally coded; _Ieduc_0 omitted)
i.round           _Iround_3-5         (naturally coded; _Iround_3 omitted)
i.dist            _Idist_1-119        (naturally coded; _Idist_1 omitted)
note: _Ieduc_9 omitted because of collinearity
note: _Iround_5 omitted because of collinearity
note: _Idist_11 omitted because of collinearity
note: _Idist_15 omitted because of collinearity
note: _Idist_41 omitted because of collinearity
note: _Idist_59 omitted because of collinearity
note: _Idist_62 omitted because of collinearity
note: _Idist_70 omitted because of collinearity
note: _Idist_75 omitted because of collinearity
note: _Idist_96 omitted because of collinearity
note: _Idist_97 omitted because of collinearity
note: _Idist_104 omitted because of collinearity
note: _Idist_113 omitted because of collinearity
note: _Idist_115 omitted because of collinearity

Linear regression                                      Number of obs =    2764
                                                       F( 14,   106) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.0571
                                                       Root MSE      =  .35302

                                 (Std. Err. adjusted for 107 clusters in dist)
------------------------------------------------------------------------------
             |               Robust
     turnout |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     celluse |   .0025719   .0191116     0.13   0.893    -.0353187    .0404626
      gender |    .016745   .0126613     1.32   0.189    -.0083571    .0418472
    _Ieduc_1 |   .0951179   .0405268     2.35   0.021     .0147696    .1754662
    _Ieduc_2 |    .016332   .0262935     0.62   0.536    -.0357975    .0684614
    _Ieduc_3 |  -.0091548   .0281981    -0.32   0.746    -.0650603    .0467507
    _Ieduc_4 |     .01162   .0296891     0.39   0.696    -.0472415    .0704815
    _Ieduc_5 |  -.0243959   .0314174    -0.78   0.439    -.0866839    .0378921
    _Ieduc_6 |  -.0480426   .0508175    -0.95   0.347    -.1487933     .052708
    _Ieduc_7 |  -.0777154   .0440001    -1.77   0.080    -.1649498     .009519
    _Ieduc_8 |   .0595889   .0664449     0.90   0.372    -.0721446    .1913225
    _Ieduc_9 |          0  (omitted)
       radio |   .0246855    .019603     1.26   0.211    -.0141795    .0635504
          tv |   .0387627   .0217648     1.78   0.078    -.0043881    .0819134
       motor |   .0217401   .0192155     1.13   0.260    -.0163564    .0598367
         job |   .0210688    .016103     1.31   0.194    -.0108568    .0529945
   _Iround_4 |   -.011958    .027913    -0.43   0.669    -.0672981    .0433822
   _Iround_5 |          0  (omitted)
    _Idist_2 |  -.0084517   .0202284    -0.42   0.677    -.0485564     .031653
    _Idist_3 |   .0060685   .0219373     0.28   0.783    -.0374244    .0495614
    _Idist_4 |  -.0409901   .0223364    -1.84   0.069    -.0852742     .003294
    _Idist_5 |   .0911873   .0313957     2.90   0.004     .0289422    .1534323
    _Idist_6 |     .02882   .0296048     0.97   0.333    -.0298743    .0875144
    _Idist_7 |  -.1270383   .0208008    -6.11   0.000    -.1682778   -.0857987
    _Idist_8 |  -.0075837    .017355    -0.44   0.663    -.0419916    .0268243
    _Idist_9 |   .1512735    .013848    10.92   0.000     .1238186    .1787284
   _Idist_10 |   .0567497   .0256044     2.22   0.029     .0059865     .107513
   _Idist_11 |          0  (omitted)
   _Idist_12 |  -.0128746   .0149635    -0.86   0.392    -.0425412     .016792
   _Idist_13 |   .0569353   .0147173     3.87   0.000     .0277569    .0861137
   _Idist_14 |   .0874139   .0317868     2.75   0.007     .0243934    .1504343
   _Idist_15 |          0  (omitted)
   _Idist_16 |   .0855434   .0164801     5.19   0.000     .0528701    .1182167
   _Idist_17 |   .0016898   .0196485     0.09   0.932    -.0372653    .0406448
   _Idist_18 |  -.0198796    .026389    -0.75   0.453    -.0721982    .0324391
   _Idist_19 |   .0698536   .0084226     8.29   0.000      .053155    .0865521
   _Idist_20 |  -.1139783   .0138342    -8.24   0.000    -.1414059   -.0865506
   _Idist_21 |   .1283124   .0251396     5.10   0.000     .0784707    .1781541
   _Idist_22 |  -.0958194   .0213504    -4.49   0.000    -.1381486   -.0534902
   _Idist_23 |   .0344992   .0181936     1.90   0.061    -.0015713    .0705698
   _Idist_24 |  -.2009746   .0174142   -11.54   0.000    -.2354999   -.1664493
   _Idist_25 |  -.1049245   .0224016    -4.68   0.000    -.1493377   -.0605112
   _Idist_26 |  -.0198332   .0223096    -0.89   0.376    -.0640641    .0243977
   _Idist_27 |  -.0974524   .0208575    -4.67   0.000    -.1388045   -.0561003
   _Idist_28 |   .0512922   .0166492     3.08   0.003     .0182835    .0843009
   _Idist_29 |   .0902245   .0238878     3.78   0.000     .0428647    .1375843
   _Idist_30 |   .0890046   .0143704     6.19   0.000     .0605138    .1174954
   _Idist_31 |   -.271842   .0342203    -7.94   0.000     -.339687    -.203997
   _Idist_32 |  -.0237813   .0182072    -1.31   0.194    -.0598788    .0123163
   _Idist_33 |  -.0582614   .0239184    -2.44   0.017     -.105682   -.0108409
   _Idist_34 |   .1273493   .0220884     5.77   0.000      .083557    .1711416
   _Idist_35 |  -.0778816   .0205549    -3.79   0.000    -.1186337   -.0371294
   _Idist_36 |  -.0684863   .0120468    -5.69   0.000    -.0923704   -.0446023
   _Idist_37 |   .1071367   .0144049     7.44   0.000     .0785776    .1356959
   _Idist_38 |     -.0807   .0204625    -3.94   0.000    -.1212689   -.0401311
   _Idist_39 |   .0535773   .0285647     1.88   0.063    -.0030551    .1102097
   _Idist_40 |    .019451   .0222836     0.87   0.385    -.0247284    .0636305
   _Idist_41 |          0  (omitted)
   _Idist_42 |   .0078767   .0119995     0.66   0.513    -.0159135    .0316669
   _Idist_43 |   .1544839   .0127013    12.16   0.000     .1293024    .1796654
   _Idist_44 |   .0212809   .0222776     0.96   0.342    -.0228867    .0654484
   _Idist_45 |   .0486329   .0280973     1.73   0.086    -.0070726    .1043385
   _Idist_46 |   .0517256   .0294287     1.76   0.082    -.0066197    .1100709
   _Idist_47 |   .0835735   .0329852     2.53   0.013     .0181771    .1489699
   _Idist_48 |  -.0622741   .0254292    -2.45   0.016      -.11269   -.0118582
   _Idist_49 |   .0669778   .0128784     5.20   0.000     .0414452    .0925103
   _Idist_50 |  -.0240531   .0281874    -0.85   0.395    -.0799374    .0318312
   _Idist_51 |  -.0569259   .0208256    -2.73   0.007    -.0982147   -.0156371
   _Idist_52 |   .0836995   .0310894     2.69   0.008     .0220617    .1453372
   _Idist_53 |   .0518347     .03056     1.70   0.093    -.0087534    .1124229
   _Idist_54 |   .0146854   .0138951     1.06   0.293    -.0128629    .0422337
   _Idist_55 |    .162277   .0111941    14.50   0.000     .1400835    .1844705
   _Idist_56 |  -.0313268   .0288236    -1.09   0.280    -.0884723    .0258188
   _Idist_57 |  -.0529529   .0149632    -3.54   0.001    -.0826189    -.023287
   _Idist_58 |   .0938415   .0260093     3.61   0.000     .0422757    .1454074
   _Idist_59 |          0  (omitted)
   _Idist_60 |   .1633109   .0270241     6.04   0.000     .1097331    .2168888
   _Idist_61 |  -.0051222   .0209517    -0.24   0.807    -.0466609    .0364166
   _Idist_62 |          0  (omitted)
   _Idist_63 |   .0909136   .0112094     8.11   0.000     .0686899    .1131373
   _Idist_64 |   .0477665   .0291437     1.64   0.104    -.0100138    .1055467
   _Idist_65 |  -.1046315   .0239668    -4.37   0.000     -.152148   -.0571151
   _Idist_66 |  -.1086192   .0282538    -3.84   0.000     -.164635   -.0526033
   _Idist_67 |   .1295567   .0153967     8.41   0.000     .0990313    .1600821
   _Idist_68 |  -.0088769   .0246846    -0.36   0.720    -.0578164    .0400626
   _Idist_69 |   .0549811   .0276233     1.99   0.049     .0002152    .1097471
   _Idist_70 |          0  (omitted)
   _Idist_71 |  -.0211836   .0082135    -2.58   0.011    -.0374676   -.0048996
   _Idist_72 |  -.2809475    .031296    -8.98   0.000    -.3429948   -.2189003
   _Idist_73 |    .025847   .0185947     1.39   0.167    -.0110188    .0627129
   _Idist_74 |  -.2333651   .0257064    -9.08   0.000    -.2843305   -.1823997
   _Idist_75 |          0  (omitted)
   _Idist_76 |   .0312202   .0199608     1.56   0.121    -.0083541    .0707945
   _Idist_77 |   .1567029   .0137094    11.43   0.000     .1295226    .1838831
   _Idist_78 |   .1161919    .021862     5.31   0.000     .0728483    .1595354
   _Idist_79 |   .0405496   .0122607     3.31   0.001     .0162415    .0648577
   _Idist_80 |  -.0414225   .0177397    -2.34   0.021    -.0765932   -.0062519
   _Idist_81 |  -.2127909   .0319224    -6.67   0.000    -.2760802   -.1495017
   _Idist_82 |  -.0285144   .0150202    -1.90   0.060    -.0582934    .0012647
   _Idist_83 |  -.2811586   .0138819   -20.25   0.000    -.3086808   -.2536364
   _Idist_84 |   .0187728   .0147281     1.27   0.205    -.0104272    .0479727
   _Idist_85 |   .1362488   .0104816    13.00   0.000     .1154679    .1570297
   _Idist_86 |  -.0062409   .0243895    -0.26   0.799    -.0545955    .0421137
   _Idist_87 |   .1231155   .0161492     7.62   0.000      .091098    .1551329
   _Idist_88 |   .0135363   .0190921     0.71   0.480    -.0243156    .0513882
   _Idist_89 |  -.0232593   .0144071    -1.61   0.109    -.0518229    .0053042
   _Idist_90 |  -.0578586   .0160715    -3.60   0.000    -.0897219   -.0259952
   _Idist_91 |   .1416247   .0268156     5.28   0.000       .08846    .1947893
   _Idist_92 |   .0142685   .0175353     0.81   0.418     -.020497     .049034
   _Idist_93 |    .059945    .019933     3.01   0.003      .020426     .099464
   _Idist_94 |   .0865001   .0321748     2.69   0.008     .0227105    .1502897
   _Idist_95 |   .0683867   .0260168     2.63   0.010      .016806    .1199675
   _Idist_96 |          0  (omitted)
   _Idist_97 |          0  (omitted)
   _Idist_98 |  -.0984298    .008227   -11.96   0.000    -.1147406   -.0821191
   _Idist_99 |   .0470191   .0208932     2.25   0.026     .0055962    .0884419
  _Idist_100 |   .0991613   .0308314     3.22   0.002     .0380352    .1602875
  _Idist_101 |   .0341165   .0136399     2.50   0.014     .0070742    .0611589
  _Idist_102 |   .1328874   .0300447     4.42   0.000     .0733208    .1924539
  _Idist_103 |  -.0156063    .022228    -0.70   0.484    -.0596755    .0284628
  _Idist_104 |          0  (omitted)
  _Idist_105 |  -.0453497   .0201282    -2.25   0.026    -.0852558   -.0054436
  _Idist_106 |    .034581   .0109663     3.15   0.002     .0128393    .0563227
  _Idist_107 |    .122343   .0153206     7.99   0.000     .0919685    .1527175
  _Idist_108 |   .0182157   .0277799     0.66   0.513    -.0368606    .0732919
  _Idist_109 |  -.0329572   .0170663    -1.93   0.056    -.0667928    .0008785
  _Idist_110 |  -.0673421   .0250589    -2.69   0.008    -.1170239   -.0176603
  _Idist_111 |   .1746407   .0138869    12.58   0.000     .1471086    .2021727
  _Idist_112 |  -.0801676   .0162665    -4.93   0.000    -.1124176   -.0479176
  _Idist_113 |          0  (omitted)
  _Idist_114 |  -.0107119   .0121504    -0.88   0.380    -.0348012    .0133773
  _Idist_115 |          0  (omitted)
  _Idist_116 |   .0024969   .0172702     0.14   0.885    -.0317429    .0367366
  _Idist_117 |  -.0068576   .0272986    -0.25   0.802    -.0609798    .0472646
  _Idist_118 |  -.0570002   .0218208    -2.61   0.010     -.100262   -.0137384
  _Idist_119 |  -.0853017   .0229941    -3.71   0.000    -.1308897   -.0397136
       _cons |    .804616   .0397658    20.23   0.000     .7257764    .8834557
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     turnout |      2764    .8516643    .3554968          0          1
i.educ            _Ieduc_0-9          (naturally coded; _Ieduc_0 omitted)
i.round           _Iround_3-5         (naturally coded; _Iround_3 omitted)
i.dist            _Idist_1-119        (naturally coded; _Idist_1 omitted)
note: _Ieduc_9 omitted because of collinearity
note: _Iround_5 omitted because of collinearity
note: _Idist_3 omitted because of collinearity
note: _Idist_4 omitted because of collinearity
note: _Idist_6 omitted because of collinearity
note: _Idist_7 omitted because of collinearity
note: _Idist_10 omitted because of collinearity
note: _Idist_11 omitted because of collinearity
note: _Idist_12 omitted because of collinearity
note: _Idist_13 omitted because of collinearity
note: _Idist_14 omitted because of collinearity
note: _Idist_15 omitted because of collinearity
note: _Idist_17 omitted because of collinearity
note: _Idist_20 omitted because of collinearity
note: _Idist_23 omitted because of collinearity
note: _Idist_24 omitted because of collinearity
note: _Idist_25 omitted because of collinearity
note: _Idist_28 omitted because of collinearity
note: _Idist_30 omitted because of collinearity
note: _Idist_31 omitted because of collinearity
note: _Idist_32 omitted because of collinearity
note: _Idist_33 omitted because of collinearity
note: _Idist_34 omitted because of collinearity
note: _Idist_35 omitted because of collinearity
note: _Idist_36 omitted because of collinearity
note: _Idist_38 omitted because of collinearity
note: _Idist_39 omitted because of collinearity
note: _Idist_41 omitted because of collinearity
note: _Idist_43 omitted because of collinearity
note: _Idist_45 omitted because of collinearity
note: _Idist_46 omitted because of collinearity
note: _Idist_47 omitted because of collinearity
note: _Idist_49 omitted because of collinearity
note: _Idist_50 omitted because of collinearity
note: _Idist_51 omitted because of collinearity
note: _Idist_54 omitted because of collinearity
note: _Idist_55 omitted because of collinearity
note: _Idist_58 omitted because of collinearity
note: _Idist_59 omitted because of collinearity
note: _Idist_60 omitted because of collinearity
note: _Idist_61 omitted because of collinearity
note: _Idist_62 omitted because of collinearity
note: _Idist_63 omitted because of collinearity
note: _Idist_66 omitted because of collinearity
note: _Idist_67 omitted because of collinearity
note: _Idist_68 omitted because of collinearity
note: _Idist_69 omitted because of collinearity
note: _Idist_70 omitted because of collinearity
note: _Idist_71 omitted because of collinearity
note: _Idist_72 omitted because of collinearity
note: _Idist_75 omitted because of collinearity
note: _Idist_76 omitted because of collinearity
note: _Idist_77 omitted because of collinearity
note: _Idist_78 omitted because of collinearity
note: _Idist_79 omitted because of collinearity
note: _Idist_82 omitted because of collinearity
note: _Idist_85 omitted because of collinearity
note: _Idist_86 omitted because of collinearity
note: _Idist_87 omitted because of collinearity
note: _Idist_90 omitted because of collinearity
note: _Idist_91 omitted because of collinearity
note: _Idist_92 omitted because of collinearity
note: _Idist_93 omitted because of collinearity
note: _Idist_95 omitted because of collinearity
note: _Idist_96 omitted because of collinearity
note: _Idist_97 omitted because of collinearity
note: _Idist_98 omitted because of collinearity
note: _Idist_99 omitted because of collinearity
note: _Idist_100 omitted because of collinearity
note: _Idist_101 omitted because of collinearity
note: _Idist_104 omitted because of collinearity
note: _Idist_105 omitted because of collinearity
note: _Idist_108 omitted because of collinearity
note: _Idist_109 omitted because of collinearity
note: _Idist_113 omitted because of collinearity
note: _Idist_114 omitted because of collinearity
note: _Idist_115 omitted because of collinearity
note: _Idist_116 omitted because of collinearity
note: _Idist_117 omitted because of collinearity
note: _Idist_118 omitted because of collinearity
note: _Idist_119 omitted because of collinearity

Linear regression                                      Number of obs =    1102
                                                       F( 14,    39) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.0586
                                                       Root MSE      =  .35868

                                  (Std. Err. adjusted for 40 clusters in dist)
------------------------------------------------------------------------------
             |               Robust
     turnout |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     celluse |   .0003168   .0277527     0.01   0.991    -.0558183    .0564519
      gender |   .0360192    .021451     1.68   0.101    -.0073696    .0794079
    _Ieduc_1 |   .0852084   .0622531     1.37   0.179    -.0407104    .2111271
    _Ieduc_2 |    -.00703   .0381813    -0.18   0.855     -.084259     .070199
    _Ieduc_3 |  -.0445149   .0362898    -1.23   0.227     -.117918    .0288882
    _Ieduc_4 |  -.0237448   .0431672    -0.55   0.585    -.1110587    .0635692
    _Ieduc_5 |  -.0677183     .04139    -1.64   0.110    -.1514374    .0160008
    _Ieduc_6 |  -.0478413   .0584629    -0.82   0.418    -.1660936     .070411
    _Ieduc_7 |  -.1814426   .0853789    -2.13   0.040    -.3541377   -.0087474
    _Ieduc_8 |  -.1358577   .1761075    -0.77   0.445    -.4920688    .2203533
    _Ieduc_9 |          0  (omitted)
       radio |   .0278609   .0324619     0.86   0.396    -.0377996    .0935213
          tv |  -.0047886   .0333241    -0.14   0.886     -.072193    .0626157
       motor |   .0663615   .0317897     2.09   0.043     .0020608    .1306622
         job |   .0406278   .0284954     1.43   0.162    -.0170095    .0982651
   _Iround_4 |  -.0413237   .0509606    -0.81   0.422    -.1444013    .0617538
   _Iround_5 |          0  (omitted)
    _Idist_2 |  -.0285142   .0361412    -0.79   0.435    -.1016166    .0445882
    _Idist_3 |          0  (omitted)
    _Idist_4 |          0  (omitted)
    _Idist_5 |   .0570355   .0566395     1.01   0.320    -.0575288    .1715997
    _Idist_6 |          0  (omitted)
    _Idist_7 |          0  (omitted)
    _Idist_8 |    .006775   .0206379     0.33   0.744    -.0349691    .0485192
    _Idist_9 |   .1833843   .0197044     9.31   0.000     .1435285    .2232402
   _Idist_10 |          0  (omitted)
   _Idist_11 |          0  (omitted)
   _Idist_12 |          0  (omitted)
   _Idist_13 |          0  (omitted)
   _Idist_14 |          0  (omitted)
   _Idist_15 |          0  (omitted)
   _Idist_16 |   .0689311   .0273114     2.52   0.016     .0136887    .1241735
   _Idist_17 |          0  (omitted)
   _Idist_18 |  -.0575349   .0459192    -1.25   0.218    -.1504152    .0353453
   _Idist_19 |   .0620009   .0113803     5.45   0.000     .0389822    .0850197
   _Idist_20 |          0  (omitted)
   _Idist_21 |   .1158803   .0410992     2.82   0.008     .0327493    .1990114
   _Idist_22 |  -.0851425   .0359418    -2.37   0.023    -.1578417   -.0124433
   _Idist_23 |          0  (omitted)
   _Idist_24 |          0  (omitted)
   _Idist_25 |          0  (omitted)
   _Idist_26 |  -.0221378   .0380937    -0.58   0.564    -.0991897    .0549141
   _Idist_27 |  -.0949195   .0353933    -2.68   0.011    -.1665092   -.0233298
   _Idist_28 |          0  (omitted)
   _Idist_29 |   .0784898   .0396268     1.98   0.055     -.001663    .1586427
   _Idist_30 |          0  (omitted)
   _Idist_31 |          0  (omitted)
   _Idist_32 |          0  (omitted)
   _Idist_33 |          0  (omitted)
   _Idist_34 |          0  (omitted)
   _Idist_35 |          0  (omitted)
   _Idist_36 |          0  (omitted)
   _Idist_37 |   .0913896   .0229712     3.98   0.000      .044926    .1378532
   _Idist_38 |          0  (omitted)
   _Idist_39 |          0  (omitted)
   _Idist_40 |   .0014585   .0348803     0.04   0.967    -.0690936    .0720107
   _Idist_41 |          0  (omitted)
   _Idist_42 |  -.0056998   .0205721    -0.28   0.783    -.0473108    .0359112
   _Idist_43 |          0  (omitted)
   _Idist_44 |  -.0060262   .0381774    -0.16   0.875    -.0832473    .0711948
   _Idist_45 |          0  (omitted)
   _Idist_46 |          0  (omitted)
   _Idist_47 |          0  (omitted)
   _Idist_48 |  -.0833977   .0452566    -1.84   0.073    -.1749379    .0081424
   _Idist_49 |          0  (omitted)
   _Idist_50 |          0  (omitted)
   _Idist_51 |          0  (omitted)
   _Idist_52 |   .0494675   .0545373     0.91   0.370    -.0608446    .1597796
   _Idist_53 |   .0376137   .0486905     0.77   0.444    -.0608721    .1360996
   _Idist_54 |          0  (omitted)
   _Idist_55 |          0  (omitted)
   _Idist_56 |  -.0651424   .0517299    -1.26   0.215    -.1697759    .0394911
   _Idist_57 |  -.0633881   .0234507    -2.70   0.010    -.1108216   -.0159547
   _Idist_58 |          0  (omitted)
   _Idist_59 |          0  (omitted)
   _Idist_60 |          0  (omitted)
   _Idist_61 |          0  (omitted)
   _Idist_62 |          0  (omitted)
   _Idist_63 |          0  (omitted)
   _Idist_64 |   .0245041   .0512126     0.48   0.635    -.0790831    .1280914
   _Idist_65 |  -.0940261   .0382599    -2.46   0.019    -.1714141    -.016638
   _Idist_66 |          0  (omitted)
   _Idist_67 |          0  (omitted)
   _Idist_68 |          0  (omitted)
   _Idist_69 |          0  (omitted)
   _Idist_70 |          0  (omitted)
   _Idist_71 |          0  (omitted)
   _Idist_72 |          0  (omitted)
   _Idist_73 |   .0500646     .02249     2.23   0.032     .0045742     .095555
   _Idist_74 |  -.2408942   .0458028    -5.26   0.000    -.3335391   -.1482493
   _Idist_75 |          0  (omitted)
   _Idist_76 |          0  (omitted)
   _Idist_77 |          0  (omitted)
   _Idist_78 |          0  (omitted)
   _Idist_79 |          0  (omitted)
   _Idist_80 |  -.0610033   .0311682    -1.96   0.058    -.1240469    .0020402
   _Idist_81 |  -.1876396   .0532303    -3.53   0.001     -.295308   -.0799712
   _Idist_82 |          0  (omitted)
   _Idist_83 |  -.2912664     .01874   -15.54   0.000    -.3291715   -.2533612
   _Idist_84 |    .018069   .0228811     0.79   0.434    -.0282124    .0643505
   _Idist_85 |          0  (omitted)
   _Idist_86 |          0  (omitted)
   _Idist_87 |          0  (omitted)
   _Idist_88 |  -.0044691   .0358282    -0.12   0.901    -.0769385    .0680002
   _Idist_89 |  -.0326262    .024798    -1.32   0.196     -.082785    .0175326
   _Idist_90 |          0  (omitted)
   _Idist_91 |          0  (omitted)
   _Idist_92 |          0  (omitted)
   _Idist_93 |          0  (omitted)
   _Idist_94 |    .044557   .0551442     0.81   0.424    -.0669827    .1560968
   _Idist_95 |          0  (omitted)
   _Idist_96 |          0  (omitted)
   _Idist_97 |          0  (omitted)
   _Idist_98 |          0  (omitted)
   _Idist_99 |          0  (omitted)
  _Idist_100 |          0  (omitted)
  _Idist_101 |          0  (omitted)
  _Idist_102 |   .1265185   .0496118     2.55   0.015     .0261692    .2268678
  _Idist_103 |  -.0389397   .0376461    -1.03   0.307    -.1150861    .0372067
  _Idist_104 |          0  (omitted)
  _Idist_105 |          0  (omitted)
  _Idist_106 |   .0162973   .0210189     0.78   0.443    -.0262175     .058812
  _Idist_107 |   .1047055   .0239105     4.38   0.000     .0563419    .1530691
  _Idist_108 |          0  (omitted)
  _Idist_109 |          0  (omitted)
  _Idist_110 |  -.0820409   .0452825    -1.81   0.078    -.1736333    .0095516
  _Idist_111 |   .1642064   .0241461     6.80   0.000     .1153663    .2130464
  _Idist_112 |  -.0958963   .0297916    -3.22   0.003    -.1561556   -.0356371
  _Idist_113 |          0  (omitted)
  _Idist_114 |          0  (omitted)
  _Idist_115 |          0  (omitted)
  _Idist_116 |          0  (omitted)
  _Idist_117 |          0  (omitted)
  _Idist_118 |          0  (omitted)
  _Idist_119 |          0  (omitted)
       _cons |   .8452148   .0673647    12.55   0.000     .7089569    .9814728
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     turnout |      1102    .8466425    .3604954          0          1
i.educ            _Ieduc_0-9          (naturally coded; _Ieduc_0 omitted)
i.round           _Iround_3-5         (naturally coded; _Iround_3 omitted)
i.dist            _Idist_1-119        (naturally coded; _Idist_1 omitted)
note: _Ieduc_9 omitted because of collinearity
note: _Iround_4 omitted because of collinearity
note: _Iround_5 omitted because of collinearity
note: _Idist_5 omitted because of collinearity
note: _Idist_6 omitted because of collinearity
note: _Idist_10 omitted because of collinearity
note: _Idist_11 omitted because of collinearity
note: _Idist_14 omitted because of collinearity
note: _Idist_15 omitted because of collinearity
note: _Idist_18 omitted because of collinearity
note: _Idist_31 omitted because of collinearity
note: _Idist_39 omitted because of collinearity
note: _Idist_41 omitted because of collinearity
note: _Idist_45 omitted because of collinearity
note: _Idist_46 omitted because of collinearity
note: _Idist_47 omitted because of collinearity
note: _Idist_48 omitted because of collinearity
note: _Idist_50 omitted because of collinearity
note: _Idist_52 omitted because of collinearity
note: _Idist_53 omitted because of collinearity
note: _Idist_56 omitted because of collinearity
note: _Idist_58 omitted because of collinearity
note: _Idist_59 omitted because of collinearity
note: _Idist_60 omitted because of collinearity
note: _Idist_62 omitted because of collinearity
note: _Idist_64 omitted because of collinearity
note: _Idist_66 omitted because of collinearity
note: _Idist_68 omitted because of collinearity
note: _Idist_69 omitted because of collinearity
note: _Idist_70 omitted because of collinearity
note: _Idist_72 omitted because of collinearity
note: _Idist_74 omitted because of collinearity
note: _Idist_75 omitted because of collinearity
note: _Idist_81 omitted because of collinearity
note: _Idist_91 omitted because of collinearity
note: _Idist_94 omitted because of collinearity
note: _Idist_95 omitted because of collinearity
note: _Idist_96 omitted because of collinearity
note: _Idist_97 omitted because of collinearity
note: _Idist_100 omitted because of collinearity
note: _Idist_102 omitted because of collinearity
note: _Idist_104 omitted because of collinearity
note: _Idist_108 omitted because of collinearity
note: _Idist_110 omitted because of collinearity
note: _Idist_113 omitted because of collinearity
note: _Idist_115 omitted because of collinearity
note: _Idist_117 omitted because of collinearity

Linear regression                                      Number of obs =     824
                                                       F( 13,    74) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.1672
                                                       Root MSE      =  .34434

                                  (Std. Err. adjusted for 75 clusters in dist)
------------------------------------------------------------------------------
             |               Robust
     turnout |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hheadcelluse |   .0885093    .030673     2.89   0.005      .027392    .1496265
      gender |  -.0181835   .0244736    -0.74   0.460    -.0669483    .0305813
    _Ieduc_1 |     .02214   .0913817     0.24   0.809     -.159942    .2042221
    _Ieduc_2 |  -.0174771   .0475476    -0.37   0.714    -.1122176    .0772635
    _Ieduc_3 |   -.067888   .0565196    -1.20   0.234    -.1805058    .0447298
    _Ieduc_4 |  -.0198007   .0463211    -0.43   0.670    -.1120974     .072496
    _Ieduc_5 |  -.0855883   .0573784    -1.49   0.140    -.1999171    .0287406
    _Ieduc_6 |  -.0616046    .116906    -0.53   0.600     -.294545    .1713357
    _Ieduc_7 |  -.0082427   .1157696    -0.07   0.943    -.2389186    .2224333
    _Ieduc_8 |  -.1438373   .1924262    -0.75   0.457    -.5272548    .2395802
    _Ieduc_9 |          0  (omitted)
       radio |   .0739594   .0315735     2.34   0.022     .0110478     .136871
          tv |   .0350219   .0396917     0.88   0.380    -.0440655    .1141093
       motor |  -.0401538   .0429804    -0.93   0.353     -.125794    .0454865
         job |   .0387794    .028143     1.38   0.172    -.0172968    .0948556
   _Iround_4 |          0  (omitted)
   _Iround_5 |          0  (omitted)
    _Idist_2 |   .1290229   .0186491     6.92   0.000     .0918638     .166182
    _Idist_3 |  -.0306442   .0319266    -0.96   0.340    -.0942594     .032971
    _Idist_4 |  -.0987685   .0201012    -4.91   0.000    -.1388212   -.0587159
    _Idist_5 |          0  (omitted)
    _Idist_6 |          0  (omitted)
    _Idist_7 |  -.1340859   .0378235    -3.55   0.001    -.2094508    -.058721
    _Idist_8 |  -.0828103   .0291958    -2.84   0.006    -.1409842   -.0246364
    _Idist_9 |   .0944007   .0262901     3.59   0.001     .0420165    .1467848
   _Idist_10 |          0  (omitted)
   _Idist_11 |          0  (omitted)
   _Idist_12 |  -.0906809   .0253746    -3.57   0.001    -.1412408    -.040121
   _Idist_13 |    .036424   .0166205     2.19   0.032     .0033069    .0695411
   _Idist_14 |          0  (omitted)
   _Idist_15 |          0  (omitted)
   _Idist_16 |  -.0014576   .0292259    -0.05   0.960    -.0596915    .0567763
   _Idist_17 |  -.3092622   .0236252   -13.09   0.000    -.3563365   -.2621879
   _Idist_18 |          0  (omitted)
   _Idist_19 |   .0143368   .0190024     0.75   0.453    -.0235264       .0522
   _Idist_20 |   .0299733   .0226135     1.33   0.189    -.0150851    .0750317
   _Idist_21 |   .0390837   .0289709     1.35   0.181    -.0186421    .0968095
   _Idist_22 |  -.1226043   .0313038    -3.92   0.000    -.1849785   -.0602301
   _Idist_23 |   .0328247   .0306254     1.07   0.287    -.0281977    .0938471
   _Idist_24 |  -.2535778   .0233762   -10.85   0.000    -.3001558   -.2069997
   _Idist_25 |  -.3150715   .0287684   -10.95   0.000    -.3723938   -.2577492
   _Idist_26 |   .1145856   .0339625     3.37   0.001     .0469138    .1822575
   _Idist_27 |  -.1188939   .0443107    -2.68   0.009    -.2071849   -.0306028
   _Idist_28 |  -.0248195   .0277052    -0.90   0.373    -.0800234    .0303844
   _Idist_29 |   .1370203   .0240538     5.70   0.000     .0890921    .1849486
   _Idist_30 |   .1338009    .014106     9.49   0.000     .1056941    .1619076
   _Idist_31 |          0  (omitted)
   _Idist_32 |   .0651395   .0390918     1.67   0.100    -.0127526    .1430316
   _Idist_33 |  -.0788106   .0330228    -2.39   0.020      -.14461   -.0130112
   _Idist_34 |   .0886638   .0346111     2.56   0.012     .0196997    .1576279
   _Idist_35 |    .013361   .0446009     0.30   0.765    -.0755082    .1022302
   _Idist_36 |  -.1909376   .0216135    -8.83   0.000    -.2340035   -.1478717
   _Idist_37 |    .033185   .0198774     1.67   0.099    -.0064216    .0727916
   _Idist_38 |   .0124882   .0115167     1.08   0.282    -.0104594    .0354357
   _Idist_39 |          0  (omitted)
   _Idist_40 |   .0996408    .034745     2.87   0.005     .0304098    .1688717
   _Idist_41 |          0  (omitted)
   _Idist_42 |  -.1264579   .0067747   -18.67   0.000    -.1399567    -.112959
   _Idist_43 |   .0902762   .0264731     3.41   0.001     .0375274    .1430249
   _Idist_44 |   .1031252   .0155899     6.61   0.000     .0720616    .1341888
   _Idist_45 |          0  (omitted)
   _Idist_46 |          0  (omitted)
   _Idist_47 |          0  (omitted)
   _Idist_48 |          0  (omitted)
   _Idist_49 |  -.0017683   .0182758    -0.10   0.923    -.0381836    .0346469
   _Idist_50 |          0  (omitted)
   _Idist_51 |   .0942689   .0256505     3.68   0.000     .0431591    .1453787
   _Idist_52 |          0  (omitted)
   _Idist_53 |          0  (omitted)
   _Idist_54 |  -.0393784   .0277937    -1.42   0.161    -.0947585    .0160018
   _Idist_55 |   .1449631   .0224862     6.45   0.000     .1001583    .1897679
   _Idist_56 |          0  (omitted)
   _Idist_57 |   -.294848   .0316727    -9.31   0.000    -.3579572   -.2317389
   _Idist_58 |          0  (omitted)
   _Idist_59 |          0  (omitted)
   _Idist_60 |          0  (omitted)
   _Idist_61 |   .0531195   .0193263     2.75   0.008     .0146111    .0916279
   _Idist_62 |          0  (omitted)
   _Idist_63 |   .1222979   .0275954     4.43   0.000     .0673128     .177283
   _Idist_64 |          0  (omitted)
   _Idist_65 |  -.0311315   .0379983    -0.82   0.415    -.1068448    .0445818
   _Idist_66 |          0  (omitted)
   _Idist_67 |   .0695157   .0310068     2.24   0.028     .0077333    .1312981
   _Idist_68 |          0  (omitted)
   _Idist_69 |          0  (omitted)
   _Idist_70 |          0  (omitted)
   _Idist_71 |  -.0799102   .0100639    -7.94   0.000    -.0999629   -.0598575
   _Idist_72 |          0  (omitted)
   _Idist_73 |   -.034966   .0375526    -0.93   0.355    -.1097912    .0398593
   _Idist_74 |          0  (omitted)
   _Idist_75 |          0  (omitted)
   _Idist_76 |    .155897   .0276689     5.63   0.000     .1007655    .2110284
   _Idist_77 |   .1440559   .0220981     6.52   0.000     .1000244    .1880874
   _Idist_78 |   .1614077   .0221487     7.29   0.000     .1172754    .2055399
   _Idist_79 |   .0339854   .0214389     1.59   0.117    -.0087324    .0767033
   _Idist_80 |  -.3797676   .0193177   -19.66   0.000     -.418259   -.3412762
   _Idist_81 |          0  (omitted)
   _Idist_82 |  -.1248603   .0122262   -10.21   0.000    -.1492216    -.100499
   _Idist_83 |  -.3776641   .0225769   -16.73   0.000    -.4226496   -.3326786
   _Idist_84 |   .1280654   .0163808     7.82   0.000     .0954258    .1607049
   _Idist_85 |   .1108117   .0239172     4.63   0.000     .0631557    .1584678
   _Idist_86 |  -.1929927   .0206915    -9.33   0.000    -.2342214   -.1517641
   _Idist_87 |   .1557186   .0197686     7.88   0.000     .1163288    .1951084
   _Idist_88 |   .0503762   .0141382     3.56   0.001     .0222053    .0785471
   _Idist_89 |  -.1170294   .0168941    -6.93   0.000    -.1506916   -.0833671
   _Idist_90 |  -.2492587   .0158039   -15.77   0.000    -.2807485   -.2177688
   _Idist_91 |          0  (omitted)
   _Idist_92 |  -.1166569   .0334301    -3.49   0.001    -.1832679    -.050046
   _Idist_93 |  -.0825356   .0252663    -3.27   0.002    -.1328799   -.0321914
   _Idist_94 |          0  (omitted)
   _Idist_95 |          0  (omitted)
   _Idist_96 |          0  (omitted)
   _Idist_97 |          0  (omitted)
   _Idist_98 |  -.2785915   .0237055   -11.75   0.000    -.3258257   -.2313573
   _Idist_99 |   .1258864   .0273022     4.61   0.000     .0714857    .1802872
  _Idist_100 |          0  (omitted)
  _Idist_101 |   .1024036   .0210723     4.86   0.000      .060416    .1443911
  _Idist_102 |          0  (omitted)
  _Idist_103 |  -.0229279   .0200526    -1.14   0.257    -.0628835    .0170277
  _Idist_104 |          0  (omitted)
  _Idist_105 |   .0133334   .0206228     0.65   0.520    -.0277584    .0544252
  _Idist_106 |   .0221596    .018905     1.17   0.245    -.0155094    .0598285
  _Idist_107 |   .0508518   .0172676     2.94   0.004     .0164454    .0852582
  _Idist_108 |          0  (omitted)
  _Idist_109 |  -.0044439   .0204154    -0.22   0.828    -.0451225    .0362348
  _Idist_110 |          0  (omitted)
  _Idist_111 |   .1486252   .0232254     6.40   0.000     .1023475    .1949028
  _Idist_112 |  -.1390963   .0146112    -9.52   0.000    -.1682098   -.1099829
  _Idist_113 |          0  (omitted)
  _Idist_114 |   .0318938   .0142883     2.23   0.029     .0034238    .0603638
  _Idist_115 |          0  (omitted)
  _Idist_116 |  -.2319314   .0202989   -11.43   0.000    -.2723779   -.1914849
  _Idist_117 |          0  (omitted)
  _Idist_118 |  -.2708031   .0570975    -4.74   0.000    -.3845723    -.157034
  _Idist_119 |  -.2541234   .0249124   -10.20   0.000    -.3037624   -.2044844
       _cons |    .835269   .0561325    14.88   0.000     .7234227    .9471154
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     turnout |       824    .8507282    .3565727          0          1
i.educ            _Ieduc_0-9          (naturally coded; _Ieduc_0 omitted)
i.round           _Iround_3-5         (naturally coded; _Iround_3 omitted)
i.dist            _Idist_1-119        (naturally coded; _Idist_1 omitted)
note: _Ieduc_9 omitted because of collinearity
note: _Iround_4 omitted because of collinearity
note: _Iround_5 omitted because of collinearity
note: _Idist_3 omitted because of collinearity
note: _Idist_4 omitted because of collinearity
note: _Idist_5 omitted because of collinearity
note: _Idist_6 omitted because of collinearity
note: _Idist_7 omitted because of collinearity
note: _Idist_10 omitted because of collinearity
note: _Idist_11 omitted because of collinearity
note: _Idist_12 omitted because of collinearity
note: _Idist_13 omitted because of collinearity
note: _Idist_14 omitted because of collinearity
note: _Idist_15 omitted because of collinearity
note: _Idist_17 omitted because of collinearity
note: _Idist_18 omitted because of collinearity
note: _Idist_20 omitted because of collinearity
note: _Idist_23 omitted because of collinearity
note: _Idist_24 omitted because of collinearity
note: _Idist_25 omitted because of collinearity
note: _Idist_28 omitted because of collinearity
note: _Idist_30 omitted because of collinearity
note: _Idist_31 omitted because of collinearity
note: _Idist_32 omitted because of collinearity
note: _Idist_33 omitted because of collinearity
note: _Idist_34 omitted because of collinearity
note: _Idist_35 omitted because of collinearity
note: _Idist_36 omitted because of collinearity
note: _Idist_38 omitted because of collinearity
note: _Idist_39 omitted because of collinearity
note: _Idist_41 omitted because of collinearity
note: _Idist_43 omitted because of collinearity
note: _Idist_45 omitted because of collinearity
note: _Idist_46 omitted because of collinearity
note: _Idist_47 omitted because of collinearity
note: _Idist_48 omitted because of collinearity
note: _Idist_49 omitted because of collinearity
note: _Idist_50 omitted because of collinearity
note: _Idist_51 omitted because of collinearity
note: _Idist_52 omitted because of collinearity
note: _Idist_53 omitted because of collinearity
note: _Idist_54 omitted because of collinearity
note: _Idist_55 omitted because of collinearity
note: _Idist_56 omitted because of collinearity
note: _Idist_58 omitted because of collinearity
note: _Idist_59 omitted because of collinearity
note: _Idist_60 omitted because of collinearity
note: _Idist_61 omitted because of collinearity
note: _Idist_62 omitted because of collinearity
note: _Idist_63 omitted because of collinearity
note: _Idist_64 omitted because of collinearity
note: _Idist_66 omitted because of collinearity
note: _Idist_67 omitted because of collinearity
note: _Idist_68 omitted because of collinearity
note: _Idist_69 omitted because of collinearity
note: _Idist_70 omitted because of collinearity
note: _Idist_71 omitted because of collinearity
note: _Idist_72 omitted because of collinearity
note: _Idist_74 omitted because of collinearity
note: _Idist_75 omitted because of collinearity
note: _Idist_76 omitted because of collinearity
note: _Idist_77 omitted because of collinearity
note: _Idist_78 omitted because of collinearity
note: _Idist_79 omitted because of collinearity
note: _Idist_81 omitted because of collinearity
note: _Idist_82 omitted because of collinearity
note: _Idist_85 omitted because of collinearity
note: _Idist_86 omitted because of collinearity
note: _Idist_87 omitted because of collinearity
note: _Idist_90 omitted because of collinearity
note: _Idist_91 omitted because of collinearity
note: _Idist_92 omitted because of collinearity
note: _Idist_93 omitted because of collinearity
note: _Idist_94 omitted because of collinearity
note: _Idist_95 omitted because of collinearity
note: _Idist_96 omitted because of collinearity
note: _Idist_97 omitted because of collinearity
note: _Idist_98 omitted because of collinearity
note: _Idist_99 omitted because of collinearity
note: _Idist_100 omitted because of collinearity
note: _Idist_101 omitted because of collinearity
note: _Idist_102 omitted because of collinearity
note: _Idist_104 omitted because of collinearity
note: _Idist_105 omitted because of collinearity
note: _Idist_108 omitted because of collinearity
note: _Idist_109 omitted because of collinearity
note: _Idist_110 omitted because of collinearity
note: _Idist_113 omitted because of collinearity
note: _Idist_114 omitted because of collinearity
note: _Idist_115 omitted because of collinearity
note: _Idist_116 omitted because of collinearity
note: _Idist_117 omitted because of collinearity
note: _Idist_118 omitted because of collinearity
note: _Idist_119 omitted because of collinearity

Linear regression                                      Number of obs =     342
                                                       F( 11,    27) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.2136
                                                       Root MSE      =  .35037

                                  (Std. Err. adjusted for 28 clusters in dist)
------------------------------------------------------------------------------
             |               Robust
     turnout |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hheadcelluse |   .1251978   .0375487     3.33   0.002     .0481543    .2022414
      gender |  -.0204063   .0324963    -0.63   0.535    -.0870832    .0462707
    _Ieduc_1 |   .1590918   .0766239     2.08   0.048     .0018725     .316311
    _Ieduc_2 |  -.0500814   .0850621    -0.59   0.561    -.2246144    .1244516
    _Ieduc_3 |  -.0746973   .0982324    -0.76   0.454    -.2762534    .1268589
    _Ieduc_4 |  -.0365987   .0737696    -0.50   0.624    -.1879614    .1147639
    _Ieduc_5 |  -.0483101   .0885313    -0.55   0.590    -.2299614    .1333411
    _Ieduc_6 |  -.0254365   .1330993    -0.19   0.850    -.2985337    .2476607
    _Ieduc_7 |  -.1541325    .089985    -1.71   0.098    -.3387664    .0305014
    _Ieduc_8 |  -1.133588   .0988891   -11.46   0.000    -1.336491    -.930684
    _Ieduc_9 |          0  (omitted)
       radio |   .1050867   .0479748     2.19   0.037     .0066505    .2035229
          tv |  -.0628417   .0506617    -1.24   0.225    -.1667909    .0411074
       motor |   .0435171   .0616813     0.71   0.487    -.0830424    .1700766
         job |   .0533075   .0452989     1.18   0.250    -.0396381    .1462531
   _Iround_4 |          0  (omitted)
   _Iround_5 |          0  (omitted)
    _Idist_2 |   .1505766   .0272239     5.53   0.000     .0947176    .2064355
    _Idist_3 |          0  (omitted)
    _Idist_4 |          0  (omitted)
    _Idist_5 |          0  (omitted)
    _Idist_6 |          0  (omitted)
    _Idist_7 |          0  (omitted)
    _Idist_8 |  -.0416739   .0353957    -1.18   0.249    -.1142998     .030952
    _Idist_9 |   .1485614   .0274213     5.42   0.000     .0922975    .2048252
   _Idist_10 |          0  (omitted)
   _Idist_11 |          0  (omitted)
   _Idist_12 |          0  (omitted)
   _Idist_13 |          0  (omitted)
   _Idist_14 |          0  (omitted)
   _Idist_15 |          0  (omitted)
   _Idist_16 |   .0366876   .0463281     0.79   0.435    -.0583697    .1317449
   _Idist_17 |          0  (omitted)
   _Idist_18 |          0  (omitted)
   _Idist_19 |   .0388428   .0302024     1.29   0.209    -.0231273    .1008129
   _Idist_20 |          0  (omitted)
   _Idist_21 |   .0613392   .0435973     1.41   0.171    -.0281151    .1507935
   _Idist_22 |  -.0804407   .0325236    -2.47   0.020    -.1471737   -.0137077
   _Idist_23 |          0  (omitted)
   _Idist_24 |          0  (omitted)
   _Idist_25 |          0  (omitted)
   _Idist_26 |   .1521612   .0372684     4.08   0.000     .0756927    .2286296
   _Idist_27 |   .0849152   .0352168     2.41   0.023     .0126563     .157174
   _Idist_28 |          0  (omitted)
   _Idist_29 |   .1468008   .0374405     3.92   0.001     .0699792    .2236224
   _Idist_30 |          0  (omitted)
   _Idist_31 |          0  (omitted)
   _Idist_32 |          0  (omitted)
   _Idist_33 |          0  (omitted)
   _Idist_34 |          0  (omitted)
   _Idist_35 |          0  (omitted)
   _Idist_36 |          0  (omitted)
   _Idist_37 |   .0440062   .0312576     1.41   0.171    -.0201292    .1081415
   _Idist_38 |          0  (omitted)
   _Idist_39 |          0  (omitted)
   _Idist_40 |   .1710919   .0419771     4.08   0.000      .084962    .2572218
   _Idist_41 |          0  (omitted)
   _Idist_42 |  -.1413289   .0064077   -22.06   0.000    -.1544764   -.1281815
   _Idist_43 |          0  (omitted)
   _Idist_44 |   .0811895   .0226388     3.59   0.001     .0347386    .1276404
   _Idist_45 |          0  (omitted)
   _Idist_46 |          0  (omitted)
   _Idist_47 |          0  (omitted)
   _Idist_48 |          0  (omitted)
   _Idist_49 |          0  (omitted)
   _Idist_50 |          0  (omitted)
   _Idist_51 |          0  (omitted)
   _Idist_52 |          0  (omitted)
   _Idist_53 |          0  (omitted)
   _Idist_54 |          0  (omitted)
   _Idist_55 |          0  (omitted)
   _Idist_56 |          0  (omitted)
   _Idist_57 |  -.2704303   .0337556    -8.01   0.000    -.3396911   -.2011695
   _Idist_58 |          0  (omitted)
   _Idist_59 |          0  (omitted)
   _Idist_60 |          0  (omitted)
   _Idist_61 |          0  (omitted)
   _Idist_62 |          0  (omitted)
   _Idist_63 |          0  (omitted)
   _Idist_64 |          0  (omitted)
   _Idist_65 |   .0344374   .0397328     0.87   0.394    -.0470875    .1159623
   _Idist_66 |          0  (omitted)
   _Idist_67 |          0  (omitted)
   _Idist_68 |          0  (omitted)
   _Idist_69 |          0  (omitted)
   _Idist_70 |          0  (omitted)
   _Idist_71 |          0  (omitted)
   _Idist_72 |          0  (omitted)
   _Idist_73 |   .0310711   .0371478     0.84   0.410      -.04515    .1072921
   _Idist_74 |          0  (omitted)
   _Idist_75 |          0  (omitted)
   _Idist_76 |          0  (omitted)
   _Idist_77 |          0  (omitted)
   _Idist_78 |          0  (omitted)
   _Idist_79 |          0  (omitted)
   _Idist_80 |  -.3518065   .0218563   -16.10   0.000    -.3966518   -.3069612
   _Idist_81 |          0  (omitted)
   _Idist_82 |          0  (omitted)
   _Idist_83 |  -.4060166   .0370739   -10.95   0.000    -.4820859   -.3299473
   _Idist_84 |   .1650916   .0230159     7.17   0.000      .117867    .2123163
   _Idist_85 |          0  (omitted)
   _Idist_86 |          0  (omitted)
   _Idist_87 |          0  (omitted)
   _Idist_88 |   .0671298   .0163872     4.10   0.000     .0335061    .1007535
   _Idist_89 |  -.1002836   .0201702    -4.97   0.000    -.1416695   -.0588977
   _Idist_90 |          0  (omitted)
   _Idist_91 |          0  (omitted)
   _Idist_92 |          0  (omitted)
   _Idist_93 |          0  (omitted)
   _Idist_94 |          0  (omitted)
   _Idist_95 |          0  (omitted)
   _Idist_96 |          0  (omitted)
   _Idist_97 |          0  (omitted)
   _Idist_98 |          0  (omitted)
   _Idist_99 |          0  (omitted)
  _Idist_100 |          0  (omitted)
  _Idist_101 |          0  (omitted)
  _Idist_102 |          0  (omitted)
  _Idist_103 |  -.0134367   .0331089    -0.41   0.688    -.0813705    .0544972
  _Idist_104 |          0  (omitted)
  _Idist_105 |          0  (omitted)
  _Idist_106 |   .0007696   .0279665     0.03   0.978    -.0566129    .0581521
  _Idist_107 |   .0735357   .0232596     3.16   0.004     .0258111    .1212604
  _Idist_108 |          0  (omitted)
  _Idist_109 |          0  (omitted)
  _Idist_110 |          0  (omitted)
  _Idist_111 |   .1546812   .0321716     4.81   0.000     .0886707    .2206918
  _Idist_112 |  -.1381877    .018203    -7.59   0.000    -.1755371   -.1008384
  _Idist_113 |          0  (omitted)
  _Idist_114 |          0  (omitted)
  _Idist_115 |          0  (omitted)
  _Idist_116 |          0  (omitted)
  _Idist_117 |          0  (omitted)
  _Idist_118 |          0  (omitted)
  _Idist_119 |          0  (omitted)
       _cons |   .8048113   .1005814     8.00   0.000     .5984353    1.011187
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     turnout |       342    .8362573    .3705841          0          1

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_ab.xml") append sheet("ab turnout
> ") 


note: results saved to outputregs_ab.xml

. 
. matrix define means=(m_turnout_2_1, m_turnout_3_1, m_turnout_4_1, m_turnout_2_2, m_turnout_2_3
> , m_turnout_3_2, m_turnout_3_3, m_turnout_4_2, m_turnout_4_3)

. global list2="$list2" + " means"

. 
. xml_tab $list2, save("outputregs_ab.xml") append sheet("ab stats") 


note: results saved to outputregs_ab.xml

. estimates clear

. 
. sum hheadcelluse if ourprov==1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
hheadcelluse |       467    .2655246    .4420857          0          1

. *0.27
. 
. *0.73a+0.27b=0.44
. *b=a+0.13
. *b=(0.44-(0.27*0.13))+0.13=0.54
. 
. *****************************************
. *****  OA TABLES 16: CONTAMINATION  *****
. *****************************************
. 
. clear all

. set more off

. 
. use "mozdata.dta", replace

. 
. global disttreat="cemind hmind vmind"

. global out1="bsturnoutpres09 bsturnoutparl09 bsguebas09"

. global out2="bsdhlakama09 bsfrelimo09 bsrenamo09"

. 
. global list1=""

. global list2=""

. 
. foreach i in $out1 {
  2. 
.         regress `i' cemind if v==1 & time==1 & civiceduc==0 & hotline==0 & verdade==0
  3.         estimates store `i'_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_1=r(mean)
  6.         display m_`i'_1
  7.         regress `i' hmind if v==1 & time==1 & civiceduc==0 & hotline==0 & verdade==0
  8.         estimates store `i'_2
  9.         sum `i' if e(sample) & control == 1
 10.         scalar define m_`i'_2=r(mean)
 11.         display m_`i'_2
 12.         regress `i' vmind if v==1 & time==1 & civiceduc==0 & hotline==0 & verdade==0
 13.         estimates store `i'_3
 14.         sum `i' if e(sample) & control == 1
 15.         scalar define m_`i'_3=r(mean)
 16.         display m_`i'_3
 17. 
.         suest `i'_1 `i'_2 `i'_3
 18.         test [`i'_1_mean]cemind=[`i'_2_mean]hmind       
 19.         scalar define t1_`i'_3=r(p)
 20.         display t1_`i'_3
 21.         test [`i'_1_mean]cemind=[`i'_3_mean]vmind       
 22.         scalar define t2_`i'_3=r(p)
 23.         display t2_`i'_3
 24.         test [`i'_2_mean]hmind=[`i'_3_mean]vmind        
 25.         scalar define t3_`i'_3=r(p)
 26.         display t3_`i'_3
 27.         test [`i'_1_mean]cemind [`i'_2_mean]hmind [`i'_3_mean]vmind     
 28.         scalar define t4_`i'_3=r(p)
 29.         display t4_`i'_3
 30.         
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 31. 
. }

      Source |       SS       df       MS              Number of obs =      41
-------------+------------------------------           F(  1,    39) =    1.31
       Model |  .018682879     1  .018682879           Prob > F      =  0.2587
    Residual |  .554728891    39  .014223818           R-squared     =  0.0326
-------------+------------------------------           Adj R-squared =  0.0078
       Total |   .57341177    40  .014335294           Root MSE      =  .11926

------------------------------------------------------------------------------
bsturnou~s09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      cemind |   .0889407   .0776045     1.15   0.259    -.0680292    .2459106
       _cons |   .4220783   .0243489    17.33   0.000      .372828    .4713285
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsturnou~s09 |        41    .4400518    .1197301   .2245571     .73734
.44005181

      Source |       SS       df       MS              Number of obs =      41
-------------+------------------------------           F(  1,    39) =    1.12
       Model |  .015961881     1  .015961881           Prob > F      =  0.2971
    Residual |  .557449889    39  .014293587           R-squared     =  0.0278
-------------+------------------------------           Adj R-squared =  0.0029
       Total |   .57341177    40  .014335294           Root MSE      =  .11956

------------------------------------------------------------------------------
bsturnou~s09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       hmind |  -.1215598    .115032    -1.06   0.297    -.3542341    .1111144
       _cons |   .4575616   .0249634    18.33   0.000     .4070683    .5080548
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsturnou~s09 |        41    .4400518    .1197301   .2245571     .73734
.44005181

      Source |       SS       df       MS              Number of obs =      41
-------------+------------------------------           F(  1,    39) =    3.39
       Model |  .045836254     1  .045836254           Prob > F      =  0.0733
    Residual |  .527575515    39  .013527577           R-squared     =  0.0799
-------------+------------------------------           Adj R-squared =  0.0563
       Total |   .57341177    40  .014335294           Root MSE      =  .11631

------------------------------------------------------------------------------
bsturnou~s09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       vmind |  -.2688411   .1460499    -1.84   0.073    -.5642548    .0265726
       _cons |   .4721073   .0251635    18.76   0.000     .4212093    .5230053
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsturnou~s09 |        41    .4400518    .1197301   .2245571     .73734
.44005181

Simultaneous results for bsturnoutpres09_1, bsturnoutpres09_2, bsturnoutpres09_3

                                                  Number of obs   =         41

-----------------------------------------------------------------------------------------
                        |               Robust
                        |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
bsturnoutpres09_1_mean  |
                 cemind |   .0889407   .0618412     1.44   0.150    -.0322658    .2101473
                  _cons |   .4220783   .0235666    17.91   0.000     .3758887    .4682679
------------------------+----------------------------------------------------------------
bsturnoutpres09_1_lnvar |
                  _cons |  -4.252837   .2126808   -20.00   0.000    -4.669684   -3.835991
------------------------+----------------------------------------------------------------
bsturnoutpres09_2_mean  |
                  hmind |  -.1215598   .1079261    -1.13   0.260    -.3330911    .0899714
                  _cons |   .4575616   .0200804    22.79   0.000     .4182048    .4969183
------------------------+----------------------------------------------------------------
bsturnoutpres09_2_lnvar |
                  _cons |  -4.247944   .2112813   -20.11   0.000    -4.662048   -3.833841
------------------------+----------------------------------------------------------------
bsturnoutpres09_3_mean  |
                  vmind |  -.2688411   .1074217    -2.50   0.012    -.4793838   -.0582984
                  _cons |   .4721073   .0229127    20.60   0.000     .4271994    .5170153
------------------------+----------------------------------------------------------------
bsturnoutpres09_3_lnvar |
                  _cons |  -4.303025   .2137424   -20.13   0.000    -4.721952   -3.884097
-----------------------------------------------------------------------------------------

 ( 1)  [bsturnoutpres09_1_mean]cemind - [bsturnoutpres09_2_mean]hmind = 0

           chi2(  1) =    4.47
         Prob > chi2 =    0.0344
.03440704

 ( 1)  [bsturnoutpres09_1_mean]cemind - [bsturnoutpres09_3_mean]vmind = 0

           chi2(  1) =    7.71
         Prob > chi2 =    0.0055
.00550443

 ( 1)  [bsturnoutpres09_2_mean]hmind - [bsturnoutpres09_3_mean]vmind = 0

           chi2(  1) =    0.91
         Prob > chi2 =    0.3410
.34100151

 ( 1)  [bsturnoutpres09_1_mean]cemind = 0
 ( 2)  [bsturnoutpres09_2_mean]hmind = 0
 ( 3)  [bsturnoutpres09_3_mean]vmind = 0

           chi2(  3) =   11.26
         Prob > chi2 =    0.0104
.01040512

      Source |       SS       df       MS              Number of obs =      41
-------------+------------------------------           F(  1,    39) =    1.59
       Model |  .021156438     1  .021156438           Prob > F      =  0.2150
    Residual |   .51927388    39  .013314715           R-squared     =  0.0391
-------------+------------------------------           Adj R-squared =  0.0145
       Total |  .540430318    40  .013510758           Root MSE      =  .11539

------------------------------------------------------------------------------
bsturnou~l09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      cemind |   .0946455   .0750835     1.26   0.215    -.0572253    .2465163
       _cons |   .4187011   .0235579    17.77   0.000     .3710507    .4663515
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsturnou~l09 |        41    .4378275    .1162358   .2245571   .6983862
.43782748

      Source |       SS       df       MS              Number of obs =      41
-------------+------------------------------           F(  1,    39) =    1.15
       Model |  .015475478     1  .015475478           Prob > F      =  0.2902
    Residual |   .52495484    39  .013460381           R-squared     =  0.0286
-------------+------------------------------           Adj R-squared =  0.0037
       Total |  .540430318    40  .013510758           Root MSE      =  .11602

------------------------------------------------------------------------------
bsturnou~l09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       hmind |  -.1196934   .1116289    -1.07   0.290    -.3454842    .1060975
       _cons |   .4550684   .0242249    18.79   0.000     .4060689    .5040678
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsturnou~l09 |        41    .4378275    .1162358   .2245571   .6983862
.43782748

      Source |       SS       df       MS              Number of obs =      41
-------------+------------------------------           F(  1,    39) =    3.60
       Model |  .045699454     1  .045699454           Prob > F      =  0.0651
    Residual |  .494730864    39  .012685407           R-squared     =  0.0846
-------------+------------------------------           Adj R-squared =  0.0611
       Total |  .540430318    40  .013510758           Root MSE      =  .11263

------------------------------------------------------------------------------
bsturnou~l09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       vmind |  -.2684396   .1414306    -1.90   0.065      -.55451    .0176307
       _cons |   .4698351   .0243676    19.28   0.000     .4205469    .5191233
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsturnou~l09 |        41    .4378275    .1162358   .2245571   .6983862
.43782748

Simultaneous results for bsturnoutparl09_1, bsturnoutparl09_2, bsturnoutparl09_3

                                                  Number of obs   =         41

-----------------------------------------------------------------------------------------
                        |               Robust
                        |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
bsturnoutparl09_1_mean  |
                 cemind |   .0946455   .0608079     1.56   0.120    -.0245358    .2138269
                  _cons |   .4187011     .02267    18.47   0.000     .3742687    .4631335
------------------------+----------------------------------------------------------------
bsturnoutparl09_1_lnvar |
                  _cons |  -4.318885   .1974237   -21.88   0.000    -4.705829   -3.931942
------------------------+----------------------------------------------------------------
bsturnoutparl09_2_mean  |
                  hmind |  -.1196934   .1067915    -1.12   0.262    -.3290009    .0896141
                  _cons |   .4550684   .0194685    23.37   0.000     .4169108    .4932259
------------------------+----------------------------------------------------------------
bsturnoutparl09_2_lnvar |
                  _cons |  -4.308005   .1978659   -21.77   0.000    -4.695815   -3.920195
------------------------+----------------------------------------------------------------
bsturnoutparl09_3_mean  |
                  vmind |  -.2684396   .1053573    -2.55   0.011    -.4749361   -.0619432
                  _cons |   .4698351   .0224291    20.95   0.000     .4258749    .5137954
------------------------+----------------------------------------------------------------
bsturnoutparl09_3_lnvar |
                  _cons |  -4.367303   .1983206   -22.02   0.000    -4.756004   -3.978602
-----------------------------------------------------------------------------------------

 ( 1)  [bsturnoutparl09_1_mean]cemind - [bsturnoutparl09_2_mean]hmind = 0

           chi2(  1) =    4.80
         Prob > chi2 =    0.0284
.02842527

 ( 1)  [bsturnoutparl09_1_mean]cemind - [bsturnoutparl09_3_mean]vmind = 0

           chi2(  1) =    8.21
         Prob > chi2 =    0.0042
.00416648

 ( 1)  [bsturnoutparl09_2_mean]hmind - [bsturnoutparl09_3_mean]vmind = 0

           chi2(  1) =    0.95
         Prob > chi2 =    0.3300
.33000191

 ( 1)  [bsturnoutparl09_1_mean]cemind = 0
 ( 2)  [bsturnoutparl09_2_mean]hmind = 0
 ( 3)  [bsturnoutparl09_3_mean]vmind = 0

           chi2(  3) =   12.04
         Prob > chi2 =    0.0072
.00723777

      Source |       SS       df       MS              Number of obs =      41
-------------+------------------------------           F(  1,    39) =    0.93
       Model |  .031562657     1  .031562657           Prob > F      =  0.3409
    Residual |  1.32391349    39    .0339465           R-squared     =  0.0233
-------------+------------------------------           Adj R-squared = -0.0018
       Total |  1.35547615    40  .033886904           Root MSE      =  .18425

------------------------------------------------------------------------------
  bsguebas09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      cemind |  -.1156021   .1198882    -0.96   0.341    -.3580988    .1268946
       _cons |   .7462265   .0376156    19.84   0.000     .6701417    .8223113
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  bsguebas09 |        41    .7228651     .184084   .1590538   .9491262
.72286514

      Source |       SS       df       MS              Number of obs =      41
-------------+------------------------------           F(  1,    39) =    1.91
       Model |  .063212358     1  .063212358           Prob > F      =  0.1751
    Residual |  1.29226379    39  .033134969           R-squared     =  0.0466
-------------+------------------------------           Adj R-squared =  0.0222
       Total |  1.35547615    40  .033886904           Root MSE      =  .18203

------------------------------------------------------------------------------
  bsguebas09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       hmind |  -.2419074   .1751424    -1.38   0.175    -.5961664    .1123517
       _cons |     .75771   .0380081    19.94   0.000     .6808313    .8345887
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  bsguebas09 |        41    .7228651     .184084   .1590538   .9491262
.72286514

      Source |       SS       df       MS              Number of obs =      41
-------------+------------------------------           F(  1,    39) =    0.34
       Model |  .011586902     1  .011586902           Prob > F      =  0.5653
    Residual |  1.34388924    39  .034458699           R-squared     =  0.0085
-------------+------------------------------           Adj R-squared = -0.0169
       Total |  1.35547615    40  .033886904           Root MSE      =  .18563

------------------------------------------------------------------------------
  bsguebas09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       vmind |  -.1351683   .2330991    -0.58   0.565    -.6066557    .3363191
       _cons |   .7389821   .0401616    18.40   0.000     .6577476    .8202165
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  bsguebas09 |        41    .7228651     .184084   .1590538   .9491262
.72286514

Simultaneous results for bsguebas09_1, bsguebas09_2, bsguebas09_3

                                                  Number of obs   =         41

------------------------------------------------------------------------------------
                   |               Robust
                   |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
bsguebas09_1_mean  |
            cemind |  -.1156021   .1066864    -1.08   0.279    -.3247036    .0934995
             _cons |   .7462265    .038758    19.25   0.000     .6702622    .8221908
-------------------+----------------------------------------------------------------
bsguebas09_1_lnvar |
             _cons |   -3.38297   .2685775   -12.60   0.000    -3.909372   -2.856567
-------------------+----------------------------------------------------------------
bsguebas09_2_mean  |
             hmind |  -.2419074   .1683638    -1.44   0.151    -.5718943    .0880795
             _cons |     .75771   .0371437    20.40   0.000     .6849098    .8305102
-------------------+----------------------------------------------------------------
bsguebas09_2_lnvar |
             _cons |  -3.407166   .2726638   -12.50   0.000    -3.941577   -2.872755
-------------------+----------------------------------------------------------------
bsguebas09_3_mean  |
             vmind |  -.1351683   .1996459    -0.68   0.498    -.5264671    .2561306
             _cons |   .7389821   .0385634    19.16   0.000     .6633993    .8145649
-------------------+----------------------------------------------------------------
bsguebas09_3_lnvar |
             _cons |  -3.367994   .2553053   -13.19   0.000    -3.868383   -2.867605
------------------------------------------------------------------------------------

 ( 1)  [bsguebas09_1_mean]cemind - [bsguebas09_2_mean]hmind = 0

           chi2(  1) =    0.65
         Prob > chi2 =    0.4217
.42167049

 ( 1)  [bsguebas09_1_mean]cemind - [bsguebas09_3_mean]vmind = 0

           chi2(  1) =    0.01
         Prob > chi2 =    0.9222
.92218692

 ( 1)  [bsguebas09_2_mean]hmind - [bsguebas09_3_mean]vmind = 0

           chi2(  1) =    0.26
         Prob > chi2 =    0.6071
.60712317

 ( 1)  [bsguebas09_1_mean]cemind = 0
 ( 2)  [bsguebas09_2_mean]hmind = 0
 ( 3)  [bsguebas09_3_mean]vmind = 0

           chi2(  3) =    2.35
         Prob > chi2 =    0.5022
.50219453

. 
. matrix define means=(m_bsturnoutpres09_1, m_bsturnoutpres09_2, m_bsturnoutpres09_3, m_bsturnou
> tparl09_1, m_bsturnoutparl09_2, m_bsturnoutparl09_3, m_bsguebas09_1, m_bsguebas09_2, m_bsgueba
> s09_3 \ 999, 999, t1_bsturnoutpres09_3, 999, 999, t1_bsturnoutparl09_3, 999, 999, t1_bsguebas0
> 9_3 \ 999, 999, t2_bsturnoutpres09_3, 999, 999, t2_bsturnoutparl09_3, 999, 999, t2_bsguebas09_
> 3 \ 999, 999, t3_bsturnoutpres09_3, 999, 999, t3_bsturnoutparl09_3, 999, 999, t3_bsguebas09_3 
> \ 999, 999, t4_bsturnoutpres09_3, 999, 999, t4_bsturnoutparl09_3, 999, 999, t4_bsguebas09_3)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_contam.xml") replace sheet("out1"
> ) 


note: results saved to outputregs_contam.xml

. xml_tab $list2, save("outputregs_contam.xml") append sheet("out_means1") 


note: results saved to outputregs_contam.xml

. estimates clear

. 
. global list1=""

. global list2=""

. 
. foreach i in $out2 {
  2. 
.         regress `i' cemind if v==1 & time==1 & civiceduc==0 & hotline==0 & verdade==0
  3.         estimates store `i'_1
  4.         sum `i' if e(sample) & control == 1
  5.         scalar define m_`i'_1=r(mean)
  6.         display m_`i'_1
  7.         regress `i' hmind if v==1 & time==1 & civiceduc==0 & hotline==0 & verdade==0
  8.         estimates store `i'_2
  9.         sum `i' if e(sample) & control == 1
 10.         scalar define m_`i'_2=r(mean)
 11.         display m_`i'_2
 12.         regress `i' vmind if v==1 & time==1 & civiceduc==0 & hotline==0 & verdade==0
 13.         estimates store `i'_3
 14.         sum `i' if e(sample) & control == 1
 15.         scalar define m_`i'_3=r(mean)
 16.         display m_`i'_3
 17. 
.         suest `i'_1 `i'_2 `i'_3
 18.         test [`i'_1_mean]cemind=[`i'_2_mean]hmind       
 19.         scalar define t1_`i'_3=r(p)
 20.         display t1_`i'_3
 21.         test [`i'_1_mean]cemind=[`i'_3_mean]vmind       
 22.         scalar define t2_`i'_3=r(p)
 23.         display t2_`i'_3
 24.         test [`i'_2_mean]hmind=[`i'_3_mean]vmind        
 25.         scalar define t3_`i'_3=r(p)
 26.         display t3_`i'_3
 27.         test [`i'_1_mean]cemind [`i'_2_mean]hmind [`i'_3_mean]vmind     
 28.         scalar define t4_`i'_3=r(p)
 29.         display t4_`i'_3
 30.                 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 31. 
. }

      Source |       SS       df       MS              Number of obs =      41
-------------+------------------------------           F(  1,    39) =    0.89
       Model |  .015706241     1  .015706241           Prob > F      =  0.3516
    Residual |  .689141442    39  .017670293           R-squared     =  0.0223
-------------+------------------------------           Adj R-squared = -0.0028
       Total |  .704847683    40  .017621192           Root MSE      =  .13293

------------------------------------------------------------------------------
bsdhlakama09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      cemind |   .0815483   .0864969     0.94   0.352    -.0934082    .2565049
       _cons |   .0974761   .0271389     3.59   0.001     .0425824    .1523698
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsdhlakama09 |        41    .1139558    .1327448   .0017921   .5856444
.11395576

      Source |       SS       df       MS              Number of obs =      41
-------------+------------------------------           F(  1,    39) =    0.32
       Model |  .005824881     1  .005824881           Prob > F      =  0.5719
    Residual |  .699022802    39  .017923662           R-squared     =  0.0083
-------------+------------------------------           Adj R-squared = -0.0172
       Total |  .704847683    40  .017621192           Root MSE      =  .13388

------------------------------------------------------------------------------
bsdhlakama09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       hmind |   .0734331   .1288135     0.57   0.572    -.1871169     .333983
       _cons |   .1033783   .0279542     3.70   0.001     .0468357    .1599209
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsdhlakama09 |        41    .1139558    .1327448   .0017921   .5856444
.11395576

      Source |       SS       df       MS              Number of obs =      41
-------------+------------------------------           F(  1,    39) =    0.10
       Model |  .001797779     1  .001797779           Prob > F      =  0.7538
    Residual |  .703049904    39  .018026921           R-squared     =  0.0026
-------------+------------------------------           Adj R-squared = -0.0230
       Total |  .704847683    40  .017621192           Root MSE      =  .13426

------------------------------------------------------------------------------
bsdhlakama09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       vmind |   .0532426   .1685978     0.32   0.754    -.2877786    .3942638
       _cons |   .1076073   .0290484     3.70   0.001     .0488514    .1663632
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
bsdhlakama09 |        41    .1139558    .1327448   .0017921   .5856444
.11395576

Simultaneous results for bsdhlakama09_1, bsdhlakama09_2, bsdhlakama09_3

                                                  Number of obs   =         41

--------------------------------------------------------------------------------------
                     |               Robust
                     |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
bsdhlakama09_1_mean  |
              cemind |   .0815483   .1024979     0.80   0.426    -.1193438    .2824404
               _cons |   .0974761   .0286333     3.40   0.001     .0413559    .1535963
---------------------+----------------------------------------------------------------
bsdhlakama09_1_lnvar |
               _cons |   -4.03587   .3384532   -11.92   0.000    -4.699226   -3.372514
---------------------+----------------------------------------------------------------
bsdhlakama09_2_mean  |
               hmind |   .0734331   .1215352     0.60   0.546    -.1647715    .3116376
               _cons |   .1033783   .0293542     3.52   0.000     .0458452    .1609114
---------------------+----------------------------------------------------------------
bsdhlakama09_2_lnvar |
               _cons |  -4.021634   .3324082   -12.10   0.000    -4.673142   -3.370126
---------------------+----------------------------------------------------------------
bsdhlakama09_3_mean  |
               vmind |   .0532426   .1408309     0.38   0.705    -.2227809    .3292661
               _cons |   .1076073   .0310678     3.46   0.001     .0467155    .1684991
---------------------+----------------------------------------------------------------
bsdhlakama09_3_lnvar |
               _cons |  -4.015889    .326132   -12.31   0.000    -4.655096   -3.376682
--------------------------------------------------------------------------------------

 ( 1)  [bsdhlakama09_1_mean]cemind - [bsdhlakama09_2_mean]hmind = 0

           chi2(  1) =    0.00
         Prob > chi2 =    0.9536
.95364883

 ( 1)  [bsdhlakama09_1_mean]cemind - [bsdhlakama09_3_mean]vmind = 0

           chi2(  1) =    0.03
         Prob > chi2 =    0.8719
.87186746

 ( 1)  [bsdhlakama09_2_mean]hmind - [bsdhlakama09_3_mean]vmind = 0

           chi2(  1) =    0.02
         Prob > chi2 =    0.8868
.88681068

 ( 1)  [bsdhlakama09_1_mean]cemind = 0
 ( 2)  [bsdhlakama09_2_mean]hmind = 0
 ( 3)  [bsdhlakama09_3_mean]vmind = 0

           chi2(  3) =    0.87
         Prob > chi2 =    0.8328
.83282666

      Source |       SS       df       MS              Number of obs =      41
-------------+------------------------------           F(  1,    39) =    1.54
       Model |  .053638142     1  .053638142           Prob > F      =  0.2226
    Residual |  1.36182093    39  .034918485           R-squared     =  0.0379
-------------+------------------------------           Adj R-squared =  0.0132
       Total |  1.41545907    40  .035386477           Root MSE      =  .18686

------------------------------------------------------------------------------
 bsfrelimo09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      cemind |  -.1507008   .1215924    -1.24   0.223    -.3966447     .095243
       _cons |   .7523034   .0381504    19.72   0.000      .675137    .8294698
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 bsfrelimo09 |        41    .7218491    .1881129   .1647635   .9476358
.7218491

      Source |       SS       df       MS              Number of obs =      41
-------------+------------------------------           F(  1,    39) =    3.05
       Model |  .102683653     1  .102683653           Prob > F      =  0.0886
    Residual |  1.31277542    39  .033660908           R-squared     =  0.0725
-------------+------------------------------           Adj R-squared =  0.0488
       Total |  1.41545907    40  .035386477           Root MSE      =  .18347

------------------------------------------------------------------------------
 bsfrelimo09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       hmind |  -.3083179    .176527    -1.75   0.089    -.6653774    .0487415
       _cons |   .7662599   .0383086    20.00   0.000     .6887735    .8437463
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 bsfrelimo09 |        41    .7218491    .1881129   .1647635   .9476358
.7218491

      Source |       SS       df       MS              Number of obs =      41
-------------+------------------------------           F(  1,    39) =    0.97
       Model |  .034286958     1  .034286958           Prob > F      =  0.3312
    Residual |  1.38117211    39   .03541467           R-squared     =  0.0242
-------------+------------------------------           Adj R-squared = -0.0008
       Total |  1.41545907    40  .035386477           Root MSE      =  .18819

------------------------------------------------------------------------------
 bsfrelimo09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       vmind |  -.2325175   .2363103    -0.98   0.331    -.7105003    .2454653
       _cons |   .7495735   .0407148    18.41   0.000       .66722    .8319271
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 bsfrelimo09 |        41    .7218491    .1881129   .1647635   .9476358
.7218491

Simultaneous results for bsfrelimo09_1, bsfrelimo09_2, bsfrelimo09_3

                                                  Number of obs   =         41

-------------------------------------------------------------------------------------
                    |               Robust
                    |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
bsfrelimo09_1_mean  |
             cemind |  -.1507008   .1106001    -1.36   0.173    -.3674731    .0660714
              _cons |   .7523034     .03842    19.58   0.000     .6770015    .8276053
--------------------+----------------------------------------------------------------
bsfrelimo09_1_lnvar |
              _cons |  -3.354739   .2609638   -12.86   0.000    -3.866219   -2.843259
--------------------+----------------------------------------------------------------
bsfrelimo09_2_mean  |
              hmind |  -.3083179   .1621921    -1.90   0.057    -.6262087    .0095728
              _cons |   .7662599   .0369105    20.76   0.000     .6939167    .8386031
--------------------+----------------------------------------------------------------
bsfrelimo09_2_lnvar |
              _cons |  -3.391418   .2718465   -12.48   0.000    -3.924227   -2.858609
--------------------+----------------------------------------------------------------
bsfrelimo09_3_mean  |
              vmind |  -.2325175    .203951    -1.14   0.254     -.632254    .1672191
              _cons |   .7495735   .0385033    19.47   0.000     .6741084    .8250386
--------------------+----------------------------------------------------------------
bsfrelimo09_3_lnvar |
              _cons |  -3.340629   .2488501   -13.42   0.000    -3.828366   -2.852892
-------------------------------------------------------------------------------------

 ( 1)  [bsfrelimo09_1_mean]cemind - [bsfrelimo09_2_mean]hmind = 0

           chi2(  1) =    1.15
         Prob > chi2 =    0.2843
.28429454

 ( 1)  [bsfrelimo09_1_mean]cemind - [bsfrelimo09_3_mean]vmind = 0

           chi2(  1) =    0.16
         Prob > chi2 =    0.6920
.69201787

 ( 1)  [bsfrelimo09_2_mean]hmind - [bsfrelimo09_3_mean]vmind = 0

           chi2(  1) =    0.13
         Prob > chi2 =    0.7151
.71507289

 ( 1)  [bsfrelimo09_1_mean]cemind = 0
 ( 2)  [bsfrelimo09_2_mean]hmind = 0
 ( 3)  [bsfrelimo09_3_mean]vmind = 0

           chi2(  3) =    4.07
         Prob > chi2 =    0.2541
.25407018

      Source |       SS       df       MS              Number of obs =      41
-------------+------------------------------           F(  1,    39) =    0.34
       Model |  .006517683     1  .006517683           Prob > F      =  0.5608
    Residual |  .738385437    39   .01893296           R-squared     =  0.0087
-------------+------------------------------           Adj R-squared = -0.0167
       Total |   .74490312    40  .018622578           Root MSE      =   .1376

------------------------------------------------------------------------------
  bsrenamo09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      cemind |   .0525322    .089534     0.59   0.561    -.1285675    .2336318
       _cons |   .1256596   .0280918     4.47   0.000     .0688385    .1824808
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  bsrenamo09 |        41    .1362756    .1364646          0   .5978793
.13627558

      Source |       SS       df       MS              Number of obs =      41
-------------+------------------------------           F(  1,    39) =    0.01
       Model |  .000129316     1  .000129316           Prob > F      =  0.9348
    Residual |  .744773804    39  .019096764           R-squared     =  0.0002
-------------+------------------------------           Adj R-squared = -0.0255
       Total |   .74490312    40  .018622578           Root MSE      =  .13819

------------------------------------------------------------------------------
  bsrenamo09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       hmind |   .0109415   .1329621     0.08   0.935    -.2579999    .2798828
       _cons |   .1346995   .0288545     4.67   0.000     .0763359    .1930632
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  bsrenamo09 |        41    .1362756    .1364646          0   .5978793
.13627558

      Source |       SS       df       MS              Number of obs =      41
-------------+------------------------------           F(  1,    39) =    0.01
       Model |  .000183471     1  .000183471           Prob > F      =  0.9224
    Residual |  .744719649    39  .019095376           R-squared     =  0.0002
-------------+------------------------------           Adj R-squared = -0.0254
       Total |   .74490312    40  .018622578           Root MSE      =  .13819

------------------------------------------------------------------------------
  bsrenamo09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       vmind |  -.0170089   .1735223    -0.10   0.922    -.3679908    .3339731
       _cons |   .1383036   .0298968     4.63   0.000     .0778316    .1987757
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  bsrenamo09 |        41    .1362756    .1364646          0   .5978793
.13627558

Simultaneous results for bsrenamo09_1, bsrenamo09_2, bsrenamo09_3

                                                  Number of obs   =         41

------------------------------------------------------------------------------------
                   |               Robust
                   |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
bsrenamo09_1_mean  |
            cemind |   .0525322   .1017079     0.52   0.606    -.1468116    .2518759
             _cons |   .1256596   .0296892     4.23   0.000     .0674698    .1838495
-------------------+----------------------------------------------------------------
bsrenamo09_1_lnvar |
             _cons |  -3.966851   .2971644   -13.35   0.000    -4.549282   -3.384419
-------------------+----------------------------------------------------------------
bsrenamo09_2_mean  |
             hmind |   .0109415    .121678     0.09   0.928     -.227543    .2494259
             _cons |   .1346995   .0298334     4.52   0.000     .0762272    .1931719
-------------------+----------------------------------------------------------------
bsrenamo09_2_lnvar |
             _cons |  -3.958236   .2877323   -13.76   0.000    -4.522181   -3.394291
-------------------+----------------------------------------------------------------
bsrenamo09_3_mean  |
             vmind |  -.0170089   .1444699    -0.12   0.906    -.3001646    .2661469
             _cons |   .1383036   .0314117     4.40   0.000     .0767379    .1998694
-------------------+----------------------------------------------------------------
bsrenamo09_3_lnvar |
             _cons |  -3.958309   .2842002   -13.93   0.000    -4.515331   -3.401287
------------------------------------------------------------------------------------

 ( 1)  [bsrenamo09_1_mean]cemind - [bsrenamo09_2_mean]hmind = 0

           chi2(  1) =    0.09
         Prob > chi2 =    0.7643
.76428456

 ( 1)  [bsrenamo09_1_mean]cemind - [bsrenamo09_3_mean]vmind = 0

           chi2(  1) =    0.15
         Prob > chi2 =    0.6939
.69387532

 ( 1)  [bsrenamo09_2_mean]hmind - [bsrenamo09_3_mean]vmind = 0

           chi2(  1) =    0.03
         Prob > chi2 =    0.8579
.85791705

 ( 1)  [bsrenamo09_1_mean]cemind = 0
 ( 2)  [bsrenamo09_2_mean]hmind = 0
 ( 3)  [bsrenamo09_3_mean]vmind = 0

           chi2(  3) =    0.28
         Prob > chi2 =    0.9636
.96362387

. 
. matrix define means=(m_bsdhlakama09_1, m_bsdhlakama09_2, m_bsdhlakama09_3, m_bsfrelimo09_1, m_
> bsfrelimo09_2, m_bsfrelimo09_3, m_bsrenamo09_1, m_bsrenamo09_2, m_bsrenamo09_3 \ 999, 999, t1_
> bsdhlakama09_3, 999, 999, t1_bsfrelimo09_3, 999, 999, t1_bsrenamo09_3 \ 999, 999, t2_bsdhlakam
> a09_3, 999, 999, t2_bsfrelimo09_3, 999, 999, t2_bsrenamo09_3 \ 999, 999, t3_bsdhlakama09_3, 99
> 9, 999, t3_bsfrelimo09_3, 999, 999, t3_bsrenamo09_3 \ 999, 999, t4_bsdhlakama09_3, 999, 999, t
> 4_bsfrelimo09_3, 999, 999, t4_bsrenamo09_3)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_contam.xml") append sheet("out2")
>  


note: results saved to outputregs_contam.xml

. xml_tab $list2, save("outputregs_contam.xml") append sheet("out_means2") 


note: results saved to outputregs_contam.xml

. estimates clear

. 
. *province-wide
. 
. clear all

. set more off

. 
. use mozballotfulldata.dta, replace

. 
. global ce="ce_dist"

. global h="h_dist"

. global v="v_dist"

. 
. global out1="eaturnoutpres09 eaturnoutparl09 eaguebas09"

. global out2="eadhlakama09 eafrelimo09 earenamo09"

. 
. global list1=""

. global list2=""

. 
. foreach i in $out1 {
  2. 
.         regress `i' $ce if (experim==. & cell==1) | (civiceduc==0 & hotline==0 & verdade==0)
  3.         estimates store `i'_1
  4.         sum `i' if e(sample)
  5.         scalar define m_`i'_1=r(mean)
  6.         display m_`i'_1
  7.         regress `i' $h if (experim==. & cell==1) | (civiceduc==0 & hotline==0 & verdade==0)
  8.         estimates store `i'_2
  9.         sum `i' if e(sample)
 10.         scalar define m_`i'_2=r(mean)
 11.         display m_`i'_2
 12.         regress `i' $v if (experim==. & cell==1) | (civiceduc==0 & hotline==0 & verdade==0)
 13.         estimates store `i'_3
 14.         sum `i' if e(sample)
 15.         scalar define m_`i'_3=r(mean)
 16.         display m_`i'_3
 17. 
.         suest `i'_1 `i'_2 `i'_3
 18.         test [`i'_1_mean]ce_dist=[`i'_2_mean]h_dist     
 19.         scalar define t1_`i'_3=r(p)
 20.         display t1_`i'_3
 21.         test [`i'_1_mean]ce_dist=[`i'_3_mean]v_dist     
 22.         scalar define t2_`i'_3=r(p)
 23.         display t2_`i'_3
 24.         test [`i'_2_mean]h_dist=[`i'_3_mean]v_dist      
 25.         scalar define t3_`i'_3=r(p)
 26.         display t3_`i'_3
 27.         test [`i'_1_mean]ce_dist [`i'_2_mean]h_dist [`i'_3_mean]v_dist  
 28.         scalar define t4_`i'_3=r(p)
 29.         display t4_`i'_3
 30. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 31. 
. }

      Source |       SS       df       MS              Number of obs =    1709
-------------+------------------------------           F(  1,  1707) =    0.81
       Model |  .027657328     1  .027657328           Prob > F      =  0.3690
    Residual |  58.4712001  1707  .034253779           R-squared     =  0.0005
-------------+------------------------------           Adj R-squared = -0.0001
       Total |  58.4988575  1708  .034249917           Root MSE      =  .18508

------------------------------------------------------------------------------
eaturnou~s09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     ce_dist |   .0082423   .0091727     0.90   0.369    -.0097486    .0262331
       _cons |   .4822098   .0071555    67.39   0.000     .4681753    .4962443
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
eaturnou~s09 |      1709    .4872255    .1850673   .0193548   1.164557
.48722554

      Source |       SS       df       MS              Number of obs =    1709
-------------+------------------------------           F(  1,  1707) =   12.33
       Model |  .419442103     1  .419442103           Prob > F      =  0.0005
    Residual |  58.0794154  1707  .034024262           R-squared     =  0.0072
-------------+------------------------------           Adj R-squared =  0.0066
       Total |  58.4988575  1708  .034249917           Root MSE      =  .18446

------------------------------------------------------------------------------
eaturnou~s09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      h_dist |   .0317648    .009047     3.51   0.000     .0140204    .0495092
       _cons |   .4687317   .0069031    67.90   0.000     .4551922    .4822711
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
eaturnou~s09 |      1709    .4872255    .1850673   .0193548   1.164557
.48722554

      Source |       SS       df       MS              Number of obs =    1709
-------------+------------------------------           F(  1,  1707) =   31.67
       Model |  1.06551807     1  1.06551807           Prob > F      =  0.0000
    Residual |  57.4333394  1707  .033645776           R-squared     =  0.0182
-------------+------------------------------           Adj R-squared =  0.0176
       Total |  58.4988575  1708  .034249917           Root MSE      =  .18343

------------------------------------------------------------------------------
eaturnou~s09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      v_dist |   .0505693   .0089861     5.63   0.000     .0329444    .0681943
       _cons |    .457961    .006836    66.99   0.000     .4445533    .4713688
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
eaturnou~s09 |      1709    .4872255    .1850673   .0193548   1.164557
.48722554

Simultaneous results for eaturnoutpres09_1, eaturnoutpres09_2, eaturnoutpres09_3

                                                  Number of obs   =       1709

-----------------------------------------------------------------------------------------
                        |               Robust
                        |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
eaturnoutpres09_1_mean  |
                ce_dist |   .0082423   .0093637     0.88   0.379    -.0101102    .0265947
                  _cons |   .4822098   .0075693    63.71   0.000     .4673741    .4970454
------------------------+----------------------------------------------------------------
eaturnoutpres09_1_lnvar |
                  _cons |  -3.373958   .0359857   -93.76   0.000    -3.444489   -3.303428
------------------------+----------------------------------------------------------------
eaturnoutpres09_2_mean  |
                 h_dist |   .0317648   .0091768     3.46   0.001     .0137786     .049751
                  _cons |   .4687317   .0072526    64.63   0.000     .4545168    .4829466
------------------------+----------------------------------------------------------------
eaturnoutpres09_2_lnvar |
                  _cons |  -3.380681   .0364203   -92.82   0.000    -3.452064   -3.309299
------------------------+----------------------------------------------------------------
eaturnoutpres09_3_mean  |
                 v_dist |   .0505693   .0091504     5.53   0.000     .0326349    .0685037
                  _cons |    .457961   .0072899    62.82   0.000     .4436732    .4722489
------------------------+----------------------------------------------------------------
eaturnoutpres09_3_lnvar |
                  _cons |  -3.391868   .0371481   -91.31   0.000    -3.464677   -3.319059
-----------------------------------------------------------------------------------------

 ( 1)  [eaturnoutpres09_1_mean]ce_dist - [eaturnoutpres09_2_mean]h_dist = 0

           chi2(  1) =   10.04
         Prob > chi2 =    0.0015
.00153322

 ( 1)  [eaturnoutpres09_1_mean]ce_dist - [eaturnoutpres09_3_mean]v_dist = 0

           chi2(  1) =   35.81
         Prob > chi2 =    0.0000
2.176e-09

 ( 1)  [eaturnoutpres09_2_mean]h_dist - [eaturnoutpres09_3_mean]v_dist = 0

           chi2(  1) =    5.54
         Prob > chi2 =    0.0185
.01853877

 ( 1)  [eaturnoutpres09_1_mean]ce_dist = 0
 ( 2)  [eaturnoutpres09_2_mean]h_dist = 0
 ( 3)  [eaturnoutpres09_3_mean]v_dist = 0

           chi2(  3) =   53.50
         Prob > chi2 =    0.0000
1.437e-11

      Source |       SS       df       MS              Number of obs =    1708
-------------+------------------------------           F(  1,  1706) =    1.22
       Model |  .041356197     1  .041356197           Prob > F      =  0.2700
    Residual |  57.9512866  1706  .033969101           R-squared     =  0.0007
-------------+------------------------------           Adj R-squared =  0.0001
       Total |  57.9926428  1707  .033973429           Root MSE      =  .18431

------------------------------------------------------------------------------
eaturnou~l09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     ce_dist |   .0100835   .0091386     1.10   0.270    -.0078406    .0280076
       _cons |   .4786946   .0071311    67.13   0.000     .4647081    .4926812
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
eaturnou~l09 |      1708    .4848344    .1843188   .0193548   1.164557
.48483443

      Source |       SS       df       MS              Number of obs =    1708
-------------+------------------------------           F(  1,  1706) =   13.86
       Model |  .467418431     1  .467418431           Prob > F      =  0.0002
    Residual |  57.5252244  1706  .033719358           R-squared     =  0.0081
-------------+------------------------------           Adj R-squared =  0.0075
       Total |  57.9926428  1707  .033973429           Root MSE      =  .18363

------------------------------------------------------------------------------
eaturnou~l09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      h_dist |    .033546   .0090101     3.72   0.000     .0158741    .0512179
       _cons |   .4652921   .0068769    67.66   0.000      .451804    .4787802
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
eaturnou~l09 |      1708    .4848344    .1843188   .0193548   1.164557
.48483443

      Source |       SS       df       MS              Number of obs =    1708
-------------+------------------------------           F(  1,  1706) =   34.17
       Model |  1.13872178     1  1.13872178           Prob > F      =  0.0000
    Residual |  56.8539211  1706  .033325862           R-squared     =  0.0196
-------------+------------------------------           Adj R-squared =  0.0191
       Total |  57.9926428  1707  .033973429           Root MSE      =  .18255

------------------------------------------------------------------------------
eaturnou~l09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      v_dist |   .0523086   .0089486     5.85   0.000     .0347572      .06986
       _cons |   .4545151   .0068128    66.71   0.000     .4411526    .4678775
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
eaturnou~l09 |      1708    .4848344    .1843188   .0193548   1.164557
.48483443

Simultaneous results for eaturnoutparl09_1, eaturnoutparl09_2, eaturnoutparl09_3

                                                  Number of obs   =       1708

-----------------------------------------------------------------------------------------
                        |               Robust
                        |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
eaturnoutparl09_1_mean  |
                ce_dist |   .0100835   .0093144     1.08   0.279    -.0081725    .0283394
                  _cons |   .4786946   .0075119    63.73   0.000     .4639716    .4934176
------------------------+----------------------------------------------------------------
eaturnoutparl09_1_lnvar |
                  _cons |  -3.382304   .0359848   -93.99   0.000    -3.452833   -3.311775
------------------------+----------------------------------------------------------------
eaturnoutparl09_2_mean  |
                 h_dist |    .033546   .0091356     3.67   0.000     .0156405    .0514514
                  _cons |   .4652921   .0072142    64.50   0.000     .4511525    .4794317
------------------------+----------------------------------------------------------------
eaturnoutparl09_2_lnvar |
                  _cons |  -3.389683   .0364589   -92.97   0.000    -3.461141   -3.318225
------------------------+----------------------------------------------------------------
eaturnoutparl09_3_mean  |
                 v_dist |   .0523086   .0091042     5.75   0.000     .0344648    .0701524
                  _cons |   .4545151   .0072395    62.78   0.000     .4403258    .4687043
------------------------+----------------------------------------------------------------
eaturnoutparl09_3_lnvar |
                  _cons |  -3.401422   .0371818   -91.48   0.000    -3.474297   -3.328547
-----------------------------------------------------------------------------------------

 ( 1)  [eaturnoutparl09_1_mean]ce_dist - [eaturnoutparl09_2_mean]h_dist = 0

           chi2(  1) =   10.05
         Prob > chi2 =    0.0015
.00152424

 ( 1)  [eaturnoutparl09_1_mean]ce_dist - [eaturnoutparl09_3_mean]v_dist = 0

           chi2(  1) =   35.23
         Prob > chi2 =    0.0000
2.931e-09

 ( 1)  [eaturnoutparl09_2_mean]h_dist - [eaturnoutparl09_3_mean]v_dist = 0

           chi2(  1) =    5.54
         Prob > chi2 =    0.0186
.01860843

 ( 1)  [eaturnoutparl09_1_mean]ce_dist = 0
 ( 2)  [eaturnoutparl09_2_mean]h_dist = 0
 ( 3)  [eaturnoutparl09_3_mean]v_dist = 0

           chi2(  3) =   54.93
         Prob > chi2 =    0.0000
7.105e-12

      Source |       SS       df       MS              Number of obs =    1709
-------------+------------------------------           F(  1,  1707) =   30.15
       Model |  1.61190644     1  1.61190644           Prob > F      =  0.0000
    Residual |  91.2655816  1707  .053465484           R-squared     =  0.0174
-------------+------------------------------           Adj R-squared =  0.0168
       Total |   92.877488  1708   .05437792           Root MSE      =  .23123

------------------------------------------------------------------------------
  eaguebas09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     ce_dist |   .0629233   .0114598     5.49   0.000     .0404465       .0854
       _cons |   .6330481   .0089397    70.81   0.000     .6155141     .650582
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  eaguebas09 |      1709    .6713396    .2331907   .0681636          1
.67133959

      Source |       SS       df       MS              Number of obs =    1709
-------------+------------------------------           F(  1,  1707) =    6.50
       Model |  .352502485     1  .352502485           Prob > F      =  0.0109
    Residual |  92.5249856  1707  .054203272           R-squared     =  0.0038
-------------+------------------------------           Adj R-squared =  0.0032
       Total |   92.877488  1708   .05437792           Root MSE      =  .23282

------------------------------------------------------------------------------
  eaguebas09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      h_dist |     .02912   .0114189     2.55   0.011     .0067236    .0515165
       _cons |   .6543856   .0087129    75.11   0.000     .6372965    .6714747
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  eaguebas09 |      1709    .6713396    .2331907   .0681636          1
.67133959

      Source |       SS       df       MS              Number of obs =    1709
-------------+------------------------------           F(  1,  1707) =   61.33
       Model |  3.22131799     1  3.22131799           Prob > F      =  0.0000
    Residual |  89.6561701  1707  .052522654           R-squared     =  0.0347
-------------+------------------------------           Adj R-squared =  0.0341
       Total |   92.877488  1708   .05437792           Root MSE      =  .22918

------------------------------------------------------------------------------
  eaguebas09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      v_dist |   .0879272   .0112274     7.83   0.000     .0659063    .1099482
       _cons |    .620456    .008541    72.64   0.000     .6037041    .6372079
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  eaguebas09 |      1709    .6713396    .2331907   .0681636          1
.67133959

Simultaneous results for eaguebas09_1, eaguebas09_2, eaguebas09_3

                                                  Number of obs   =       1709

------------------------------------------------------------------------------------
                   |               Robust
                   |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
eaguebas09_1_mean  |
           ce_dist |   .0629233   .0119189     5.28   0.000     .0395627    .0862838
             _cons |   .6330481   .0099152    63.85   0.000     .6136147    .6524815
-------------------+----------------------------------------------------------------
eaguebas09_1_lnvar |
             _cons |  -2.928719   .0273454  -107.10   0.000    -2.982315   -2.875123
-------------------+----------------------------------------------------------------
eaguebas09_2_mean  |
            h_dist |     .02912   .0115014     2.53   0.011     .0065777    .0516623
             _cons |   .6543856   .0089401    73.20   0.000     .6368632    .6719079
-------------------+----------------------------------------------------------------
eaguebas09_2_lnvar |
             _cons |  -2.915014   .0279498  -104.29   0.000    -2.969795   -2.860233
-------------------+----------------------------------------------------------------
eaguebas09_3_mean  |
            v_dist |   .0879272   .0115449     7.62   0.000     .0652997    .1105548
             _cons |    .620456   .0094045    65.97   0.000     .6020236    .6388884
-------------------+----------------------------------------------------------------
eaguebas09_3_lnvar |
             _cons |  -2.946511   .0274413  -107.38   0.000    -3.000295   -2.892727
------------------------------------------------------------------------------------

 ( 1)  [eaguebas09_1_mean]ce_dist - [eaguebas09_2_mean]h_dist = 0

           chi2(  1) =    6.68
         Prob > chi2 =    0.0098
.00977137

 ( 1)  [eaguebas09_1_mean]ce_dist - [eaguebas09_3_mean]v_dist = 0

           chi2(  1) =    7.44
         Prob > chi2 =    0.0064
.0063915

 ( 1)  [eaguebas09_2_mean]h_dist - [eaguebas09_3_mean]v_dist = 0

           chi2(  1) =   22.25
         Prob > chi2 =    0.0000
2.395e-06

 ( 1)  [eaguebas09_1_mean]ce_dist = 0
 ( 2)  [eaguebas09_2_mean]h_dist = 0
 ( 3)  [eaguebas09_3_mean]v_dist = 0

           chi2(  3) =   58.48
         Prob > chi2 =    0.0000
1.238e-12

. 
. matrix define means=(m_eaturnoutpres09_1, m_eaturnoutpres09_2, m_eaturnoutpres09_3, m_eaturnou
> tparl09_1, m_eaturnoutparl09_2, m_eaturnoutparl09_3, m_eaguebas09_1, m_eaguebas09_2, m_eagueba
> s09_3 \ 999, 999, t1_eaturnoutpres09_3, 999, 999, t1_eaturnoutparl09_3, 999, 999, t1_eaguebas0
> 9_3 \ 999, 999, t2_eaturnoutpres09_3, 999, 999, t2_eaturnoutparl09_3, 999, 999, t2_eaguebas09_
> 3 \ 999, 999, t3_eaturnoutpres09_3, 999, 999, t3_eaturnoutparl09_3, 999, 999, t3_eaguebas09_3 
> \ 999, 999, t4_eaturnoutpres09_3, 999, 999, t4_eaturnoutparl09_3, 999, 999, t4_eaguebas09_3)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_contam.xml") append sheet("all_ou
> t1") 


note: results saved to outputregs_contam.xml

. xml_tab $list2, save("outputregs_contam.xml") append sheet("all_out_means1") 


note: results saved to outputregs_contam.xml

. estimates clear

. 
. global list1=""

. global list2=""

. 
. foreach i in $out2 {
  2. 
.         regress `i' $ce if (experim==. & cell==1) | (civiceduc==0 & hotline==0 & verdade==0)
  3.         estimates store `i'_1
  4.         sum `i' if e(sample)
  5.         scalar define m_`i'_1=r(mean)
  6.         display m_`i'_1
  7.         regress `i' $h if (experim==. & cell==1) | (civiceduc==0 & hotline==0 & verdade==0)
  8.         estimates store `i'_2
  9.         sum `i' if e(sample)
 10.         scalar define m_`i'_2=r(mean)
 11.         display m_`i'_2
 12.         regress `i' $v if (experim==. & cell==1) | (civiceduc==0 & hotline==0 & verdade==0)
 13.         estimates store `i'_3
 14.         sum `i' if e(sample)
 15.         scalar define m_`i'_3=r(mean)
 16.         display m_`i'_3
 17. 
.         suest `i'_1 `i'_2 `i'_3
 18.         test [`i'_1_mean]ce_dist=[`i'_2_mean]h_dist     
 19.         scalar define t1_`i'_3=r(p)
 20.         display t1_`i'_3
 21.         test [`i'_1_mean]ce_dist=[`i'_3_mean]v_dist     
 22.         scalar define t2_`i'_3=r(p)
 23.         display t2_`i'_3
 24.         test [`i'_2_mean]h_dist=[`i'_3_mean]v_dist      
 25.         scalar define t3_`i'_3=r(p)
 26.         display t3_`i'_3
 27.         test [`i'_1_mean]ce_dist [`i'_2_mean]h_dist [`i'_3_mean]v_dist  
 28.         scalar define t4_`i'_3=r(p)
 29.         display t4_`i'_3
 30. 
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 31. 
. }

      Source |       SS       df       MS              Number of obs =    1709
-------------+------------------------------           F(  1,  1707) =   66.90
       Model |   2.0591195     1   2.0591195           Prob > F      =  0.0000
    Residual |  52.5381462  1707  .030778059           R-squared     =  0.0377
-------------+------------------------------           Adj R-squared =  0.0372
       Total |  54.5972657  1708  .031965612           Root MSE      =  .17544

------------------------------------------------------------------------------
eadhlakama09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     ce_dist |  -.0711184   .0086948    -8.18   0.000    -.0881721   -.0540647
       _cons |   .2030806   .0067828    29.94   0.000     .1897772    .2163841
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
eadhlakama09 |      1709     .159802    .1787893          0   .8258064
.15980201

      Source |       SS       df       MS              Number of obs =    1709
-------------+------------------------------           F(  1,  1707) =   22.20
       Model |  .700905508     1  .700905508           Prob > F      =  0.0000
    Residual |  53.8963601  1707  .031573732           R-squared     =  0.0128
-------------+------------------------------           Adj R-squared =  0.0123
       Total |  54.5972657  1708  .031965612           Root MSE      =  .17769

------------------------------------------------------------------------------
eadhlakama09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      h_dist |   -.041062   .0087151    -4.71   0.000    -.0581554   -.0239686
       _cons |   .1837088   .0066499    27.63   0.000      .170666    .1967516
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
eadhlakama09 |      1709     .159802    .1787893          0   .8258064
.15980201

      Source |       SS       df       MS              Number of obs =    1709
-------------+------------------------------           F(  1,  1707) =   80.51
       Model |  2.45893636     1  2.45893636           Prob > F      =  0.0000
    Residual |  52.1383293  1707  .030543837           R-squared     =  0.0450
-------------+------------------------------           Adj R-squared =  0.0445
       Total |  54.5972657  1708  .031965612           Root MSE      =  .17477

------------------------------------------------------------------------------
eadhlakama09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      v_dist |  -.0768211   .0085619    -8.97   0.000    -.0936139   -.0600282
       _cons |   .2042584   .0065132    31.36   0.000     .1914837    .2170332
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
eadhlakama09 |      1709     .159802    .1787893          0   .8258064
.15980201

Simultaneous results for eadhlakama09_1, eadhlakama09_2, eadhlakama09_3

                                                  Number of obs   =       1709

--------------------------------------------------------------------------------------
                     |               Robust
                     |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
eadhlakama09_1_mean  |
             ce_dist |  -.0711184   .0091585    -7.77   0.000    -.0890687   -.0531681
               _cons |   .2030806   .0077572    26.18   0.000     .1878767    .2182845
---------------------+----------------------------------------------------------------
eadhlakama09_1_lnvar |
               _cons |  -3.480953   .0391404   -88.94   0.000    -3.557667   -3.404239
---------------------+----------------------------------------------------------------
eadhlakama09_2_mean  |
              h_dist |   -.041062   .0088372    -4.65   0.000    -.0583827   -.0237413
               _cons |   .1837088   .0069789    26.32   0.000     .1700304    .1973872
---------------------+----------------------------------------------------------------
eadhlakama09_2_lnvar |
               _cons |   -3.45543   .0394948   -87.49   0.000    -3.532838   -3.378021
---------------------+----------------------------------------------------------------
eadhlakama09_3_mean  |
              v_dist |  -.0768211   .0088866    -8.64   0.000    -.0942385   -.0594036
               _cons |   .2042584   .0073855    27.66   0.000     .1897832    .2187337
---------------------+----------------------------------------------------------------
eadhlakama09_3_lnvar |
               _cons |  -3.488592   .0393863   -88.57   0.000    -3.565788   -3.411397
--------------------------------------------------------------------------------------

 ( 1)  [eadhlakama09_1_mean]ce_dist - [eadhlakama09_2_mean]h_dist = 0

           chi2(  1) =    8.28
         Prob > chi2 =    0.0040
.00400019

 ( 1)  [eadhlakama09_1_mean]ce_dist - [eadhlakama09_3_mean]v_dist = 0

           chi2(  1) =    0.67
         Prob > chi2 =    0.4124
.41238236

 ( 1)  [eadhlakama09_2_mean]h_dist - [eadhlakama09_3_mean]v_dist = 0

           chi2(  1) =   12.80
         Prob > chi2 =    0.0003
.00034671

 ( 1)  [eadhlakama09_1_mean]ce_dist = 0
 ( 2)  [eadhlakama09_2_mean]h_dist = 0
 ( 3)  [eadhlakama09_3_mean]v_dist = 0

           chi2(  3) =   82.22
         Prob > chi2 =    0.0000
1.026e-17

      Source |       SS       df       MS              Number of obs =    1708
-------------+------------------------------           F(  1,  1706) =   34.95
       Model |  1.91447423     1  1.91447423           Prob > F      =  0.0000
    Residual |    93.44792  1706  .054776038           R-squared     =  0.0201
-------------+------------------------------           Adj R-squared =  0.0195
       Total |  95.3623942  1707  .055865492           Root MSE      =  .23404

------------------------------------------------------------------------------
 eafrelimo09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     ce_dist |   .0686063   .0116047     5.91   0.000     .0458453    .0913672
       _cons |   .6120865   .0090554    67.59   0.000     .5943257    .6298474
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 eafrelimo09 |      1708    .6538608    .2363588    .074375          1
.65386083

      Source |       SS       df       MS              Number of obs =    1708
-------------+------------------------------           F(  1,  1706) =   10.88
       Model |   .60430347     1   .60430347           Prob > F      =  0.0010
    Residual |  94.7580908  1706  .055544016           R-squared     =  0.0063
-------------+------------------------------           Adj R-squared =  0.0058
       Total |  95.3623942  1707  .055865492           Root MSE      =  .23568

------------------------------------------------------------------------------
 eafrelimo09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      h_dist |    .038143    .011564     3.30   0.001      .015462     .060824
       _cons |   .6316405   .0088262    71.56   0.000     .6143292    .6489518
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 eafrelimo09 |      1708    .6538608    .2363588    .074375          1
.65386083

      Source |       SS       df       MS              Number of obs =    1708
-------------+------------------------------           F(  1,  1706) =   65.88
       Model |  3.54555051     1  3.54555051           Prob > F      =  0.0000
    Residual |  91.8168437  1706  .053819955           R-squared     =  0.0372
-------------+------------------------------           Adj R-squared =  0.0366
       Total |  95.3623942  1707  .055865492           Root MSE      =  .23199

------------------------------------------------------------------------------
 eafrelimo09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      v_dist |   .0923009    .011372     8.12   0.000     .0699964    .1146054
       _cons |   .6003609   .0086578    69.34   0.000     .5833798     .617342
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
 eafrelimo09 |      1708    .6538608    .2363588    .074375          1
.65386083

Simultaneous results for eafrelimo09_1, eafrelimo09_2, eafrelimo09_3

                                                  Number of obs   =       1708

-------------------------------------------------------------------------------------
                    |               Robust
                    |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
eafrelimo09_1_mean  |
            ce_dist |   .0686063   .0120277     5.70   0.000     .0450324    .0921802
              _cons |   .6120865   .0099551    61.48   0.000     .5925749    .6315981
--------------------+----------------------------------------------------------------
eafrelimo09_1_lnvar |
              _cons |  -2.904502   .0263381  -110.28   0.000    -2.956124   -2.852881
--------------------+----------------------------------------------------------------
eafrelimo09_2_mean  |
             h_dist |    .038143   .0116095     3.29   0.001     .0153888    .0608973
              _cons |   .6316405   .0089548    70.54   0.000     .6140894    .6491916
--------------------+----------------------------------------------------------------
eafrelimo09_2_lnvar |
              _cons |  -2.890579   .0269155  -107.39   0.000    -2.943333   -2.837826
--------------------+----------------------------------------------------------------
eafrelimo09_3_mean  |
             v_dist |   .0923009   .0116531     7.92   0.000     .0694612    .1151407
              _cons |   .6003609   .0094198    63.73   0.000     .5818985    .6188233
--------------------+----------------------------------------------------------------
eafrelimo09_3_lnvar |
              _cons |  -2.922111   .0266737  -109.55   0.000     -2.97439   -2.869831
-------------------------------------------------------------------------------------

 ( 1)  [eafrelimo09_1_mean]ce_dist - [eafrelimo09_2_mean]h_dist = 0

           chi2(  1) =    5.55
         Prob > chi2 =    0.0185
.01851415

 ( 1)  [eafrelimo09_1_mean]ce_dist - [eafrelimo09_3_mean]v_dist = 0

           chi2(  1) =    6.39
         Prob > chi2 =    0.0115
.01148671

 ( 1)  [eafrelimo09_2_mean]h_dist - [eafrelimo09_3_mean]v_dist = 0

           chi2(  1) =   19.10
         Prob > chi2 =    0.0000
.00001239

 ( 1)  [eafrelimo09_1_mean]ce_dist = 0
 ( 2)  [eafrelimo09_2_mean]h_dist = 0
 ( 3)  [eafrelimo09_3_mean]v_dist = 0

           chi2(  3) =   62.91
         Prob > chi2 =    0.0000
1.401e-13

      Source |       SS       df       MS              Number of obs =    1708
-------------+------------------------------           F(  1,  1706) =   57.28
       Model |  1.71275205     1  1.71275205           Prob > F      =  0.0000
    Residual |  51.0107394  1706  .029900785           R-squared     =  0.0325
-------------+------------------------------           Adj R-squared =  0.0319
       Total |  52.7234915  1707  .030886638           Root MSE      =  .17292

------------------------------------------------------------------------------
  earenamo09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     ce_dist |  -.0648913   .0085739    -7.57   0.000    -.0817078   -.0480747
       _cons |   .2083256   .0066904    31.14   0.000     .1952033    .2214479
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  earenamo09 |      1708    .1688133    .1757459          0   .7564576
.16881334

      Source |       SS       df       MS              Number of obs =    1708
-------------+------------------------------           F(  1,  1706) =   17.63
       Model |  .539384522     1  .539384522           Prob > F      =  0.0000
    Residual |   52.184107  1706  .030588574           R-squared     =  0.0102
-------------+------------------------------           Adj R-squared =  0.0097
       Total |  52.7234915  1707  .030886638           Root MSE      =   .1749

------------------------------------------------------------------------------
  earenamo09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      h_dist |   -.036036   .0085816    -4.20   0.000    -.0528676   -.0192045
       _cons |   .1898062   .0065499    28.98   0.000     .1769595    .2026529
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  earenamo09 |      1708    .1688133    .1757459          0   .7564576
.16881334

      Source |       SS       df       MS              Number of obs =    1708
-------------+------------------------------           F(  1,  1706) =   73.41
       Model |  2.17513596     1  2.17513596           Prob > F      =  0.0000
    Residual |  50.5483555  1706  .029629751           R-squared     =  0.0413
-------------+------------------------------           Adj R-squared =  0.0407
       Total |  52.7234915  1707  .030886638           Root MSE      =  .17213

------------------------------------------------------------------------------
  earenamo09 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      v_dist |  -.0722948   .0084378    -8.57   0.000    -.0888443   -.0557454
       _cons |   .2107173   .0064239    32.80   0.000     .1981176    .2233169
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
  earenamo09 |      1708    .1688133    .1757459          0   .7564576
.16881334

Simultaneous results for earenamo09_1, earenamo09_2, earenamo09_3

                                                  Number of obs   =       1708

------------------------------------------------------------------------------------
                   |               Robust
                   |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
earenamo09_1_mean  |
           ce_dist |  -.0648913   .0089908    -7.22   0.000    -.0825129   -.0472696
             _cons |   .2083256   .0075679    27.53   0.000     .1934928    .2231584
-------------------+----------------------------------------------------------------
earenamo09_1_lnvar |
             _cons |  -3.509871   .0380958   -92.13   0.000    -3.584537   -3.435204
-------------------+----------------------------------------------------------------
earenamo09_2_mean  |
            h_dist |   -.036036   .0086865    -4.15   0.000    -.0530613   -.0190108
             _cons |   .1898062   .0068329    27.78   0.000     .1764139    .2031985
-------------------+----------------------------------------------------------------
earenamo09_2_lnvar |
             _cons |  -3.487129   .0383342   -90.97   0.000    -3.562262   -3.411995
-------------------+----------------------------------------------------------------
earenamo09_3_mean  |
            v_dist |  -.0722948   .0087352    -8.28   0.000    -.0894155   -.0551742
             _cons |   .2107173   .0072184    29.19   0.000     .1965694    .2248651
-------------------+----------------------------------------------------------------
earenamo09_3_lnvar |
             _cons |  -3.518976   .0382817   -91.92   0.000    -3.594007   -3.443945
------------------------------------------------------------------------------------

 ( 1)  [earenamo09_1_mean]ce_dist - [earenamo09_2_mean]h_dist = 0

           chi2(  1) =    7.94
         Prob > chi2 =    0.0048
.00483183

 ( 1)  [earenamo09_1_mean]ce_dist - [earenamo09_3_mean]v_dist = 0

           chi2(  1) =    1.19
         Prob > chi2 =    0.2744
.27440717

 ( 1)  [earenamo09_2_mean]h_dist - [earenamo09_3_mean]v_dist = 0

           chi2(  1) =   13.39
         Prob > chi2 =    0.0003
.00025282

 ( 1)  [earenamo09_1_mean]ce_dist = 0
 ( 2)  [earenamo09_2_mean]h_dist = 0
 ( 3)  [earenamo09_3_mean]v_dist = 0

           chi2(  3) =   73.37
         Prob > chi2 =    0.0000
8.106e-16

. 
. matrix define means=(m_eadhlakama09_1, m_eadhlakama09_2, m_eadhlakama09_3, m_eafrelimo09_1, m_
> eafrelimo09_2, m_eafrelimo09_3, m_earenamo09_1, m_earenamo09_2, m_earenamo09_3 \ 999, 999, t1_
> eadhlakama09_3, 999, 999, t1_eafrelimo09_3, 999, 999, t1_earenamo09_3 \ 999, 999, t2_eadhlakam
> a09_3, 999, 999, t2_eafrelimo09_3, 999, 999, t2_earenamo09_3 \ 999, 999, t3_eadhlakama09_3, 99
> 9, 999, t3_eafrelimo09_3, 999, 999, t3_earenamo09_3 \ 999, 999, t4_eadhlakama09_3, 999, 999, t
> 4_eafrelimo09_3, 999, 999, t4_earenamo09_3)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("outputregs_contam.xml") append sheet("all_ou
> t2") 


note: results saved to outputregs_contam.xml

. xml_tab $list2, save("outputregs_contam.xml") append sheet("all_out_means2") 


note: results saved to outputregs_contam.xml

. estimates clear

. 
. **********************************
. *****  ATTRITION ROBUSTNESS  *****
. **********************************
. 
. clear all

. set more off

. 
. use mozdata, replace

. 
. *********************************************************
. *****  OA TABLE 14: CHARACTERISTICS OF PANEL DROPS  *****
. *********************************************************
. 
. global demo1="sex age head housen single marriedunion noschl informalschl lit prim5y sec10y"

. global demo2="chang macua lomue chuabo chironga maconde cathol protest muslim"

. global demo3="job agric com art man assal tea puboff stud dom house land cattle cel expenditur
> e"

. 
. foreach i in $demo1 {
  2. 
.         global list=""
  3. 
.         regress `i' drops2 if time==0, cluster(ea)
  4.         estimates store `i'_1
  5. 
.         global list="$list" + " `i'_1"
  6.         xml_tab $list, below save(attrition.xml) append sheet("drops `i'")
  7.         estimates clear
  8. 
. }

Linear regression                                      Number of obs =    1766
                                                       F(  1,   160) =    1.96
                                                       Prob > F      =  0.1636
                                                       R-squared     =  0.0014
                                                       Root MSE      =  .49757

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         sex |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |   .0385999   .0275823     1.40   0.164    -.0158724    .0930722
       _cons |   .4381443   .0148571    29.49   0.000     .4088029    .4674857
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1750
                                                       F(  1,   160) =    0.93
                                                       Prob > F      =  0.3375
                                                       R-squared     =  0.0005
                                                       Root MSE      =  13.584

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |   .6702084   .6967131     0.96   0.338    -.7057313    2.046148
       _cons |   37.36851   .4782259    78.14   0.000     36.42406    38.31296
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1766
                                                       F(  1,   160) =    0.06
                                                       Prob > F      =  0.8014
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .43406

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        head |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |   .0059281   .0235309     0.25   0.801     -.040543    .0523993
       _cons |   .7465636   .0135801    54.97   0.000     .7197443    .7733829
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1765
                                                       F(  1,   160) =    4.35
                                                       Prob > F      =  0.0385
                                                       R-squared     =  0.0023
                                                       Root MSE      =  2.8457

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      housen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |  -.2864463   .1372716    -2.09   0.038    -.5575441   -.0153485
       _cons |   5.968643   .0981746    60.80   0.000     5.774758    6.162528
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1766
                                                       F(  1,   160) =    0.09
                                                       Prob > F      =  0.7601
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .38208

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      single |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |   .0058054   .0189836     0.31   0.760    -.0316853     .043296
       _cons |   .1752577   .0123795    14.16   0.000     .1508095     .199706
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1766
                                                       F(  1,   160) =    0.01
                                                       Prob > F      =  0.9409
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .44687

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
marriedunion |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |   .0016868    .022718     0.07   0.941    -.0431789    .0465526
       _cons |   .7242268    .015823    45.77   0.000      .692978    .7554756
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1763
                                                       F(  1,   160) =    1.06
                                                       Prob > F      =  0.3054
                                                       R-squared     =  0.0005
                                                       Root MSE      =  .39288

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      noschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |   .0188298   .0183136     1.03   0.305    -.0173378    .0549973
       _cons |   .1841652    .013433    13.71   0.000     .1576363    .2106942
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1763
                                                       F(  1,   160) =    0.50
                                                       Prob > F      =  0.4799
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .25294

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
informalschl |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |  -.0082006   .0115807    -0.71   0.480    -.0310714    .0146701
       _cons |   .0714286   .0083902     8.51   0.000     .0548587    .0879985
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1763
                                                       F(  1,   160) =    1.06
                                                       Prob > F      =  0.3054
                                                       R-squared     =  0.0005
                                                       Root MSE      =  .39288

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         lit |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |  -.0188298   .0183136    -1.03   0.305    -.0549973    .0173378
       _cons |   .8158348    .013433    60.73   0.000     .7893058    .8423637
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1763
                                                       F(  1,   160) =    0.14
                                                       Prob > F      =  0.7055
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .45153

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      prim5y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |  -.0079013   .0208729    -0.38   0.706    -.0491232    .0333205
       _cons |   .2874355    .014179    20.27   0.000     .2594333    .3154376
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1763
                                                       F(  1,   160) =    0.00
                                                       Prob > F      =  0.9482
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .36572

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      sec10y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |  -.0011384   .0175006    -0.07   0.948    -.0357004    .0334236
       _cons |   .1592083    .011996    13.27   0.000     .1355173    .1828993
------------------------------------------------------------------------------


note: results saved to attrition.xml

. 
. foreach i in $demo2 {
  2. 
.         global list=""
  3. 
.         regress `i' drops2 if time==0, cluster(ea)
  4.         estimates store `i'_1
  5. 
.         global list="$list" + " `i'_1"
  6.         xml_tab $list, below save(attrition.xml) append sheet("drops `i'")
  7.         estimates clear
  8. 
. }

Linear regression                                      Number of obs =    1762
                                                       F(  1,   160) =    0.19
                                                       Prob > F      =  0.6631
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .47986

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       chang |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |  -.0106397    .024381    -0.44   0.663    -.0587898    .0375104
       _cons |   .3623064   .0328835    11.02   0.000     .2973647     .427248
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1762
                                                       F(  1,   160) =    1.26
                                                       Prob > F      =  0.2635
                                                       R-squared     =  0.0006
                                                       Root MSE      =   .4106

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       macua |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |   -.022031   .0196356    -1.12   0.264    -.0608094    .0167474
       _cons |    .222031   .0296336     7.49   0.000     .1635075    .2805544
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1762
                                                       F(  1,   160) =    1.98
                                                       Prob > F      =  0.1611
                                                       R-squared     =  0.0017
                                                       Root MSE      =   .2936

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       lomue |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |  -.0257975   .0183247    -1.41   0.161    -.0619869    .0103919
       _cons |   .1041308   .0212523     4.90   0.000     .0621595    .1461021
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1762
                                                       F(  1,   160) =    0.05
                                                       Prob > F      =  0.8193
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .29464

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      chuabo |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |    .003669    .016034     0.23   0.819    -.0279966    .0353345
       _cons |   .0946644    .019187     4.93   0.000     .0567718    .1325569
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1762
                                                       F(  1,   160) =    1.62
                                                       Prob > F      =  0.2044
                                                       R-squared     =  0.0008
                                                       Root MSE      =  .21787

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
    chironga |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |   .0127223   .0099828     1.27   0.204    -.0069928    .0324375
       _cons |    .045611   .0086907     5.25   0.000     .0284477    .0627743
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1762
                                                       F(  1,   160) =    1.40
                                                       Prob > F      =  0.2379
                                                       R-squared     =  0.0015
                                                       Root MSE      =  .19793

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     maconde |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |   .0163827   .0138285     1.18   0.238    -.0109272    .0436926
       _cons |    .035284   .0115447     3.06   0.003     .0124844    .0580836
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1766
                                                       F(  1,   160) =    0.12
                                                       Prob > F      =  0.7333
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .48046

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cathol |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |  -.0079203   .0232054    -0.34   0.733    -.0537487     .037908
       _cons |   .3634021   .0208662    17.42   0.000     .3221934    .4046108
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1766
                                                       F(  1,   160) =    0.73
                                                       Prob > F      =  0.3942
                                                       R-squared     =  0.0004
                                                       Root MSE      =  .47783

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
     protest |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |   .0200877   .0235123     0.85   0.394    -.0263469    .0665223
       _cons |   .3453608   .0238592    14.47   0.000     .2982413    .3924803
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1766
                                                       F(  1,   160) =    0.35
                                                       Prob > F      =  0.5574
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .41577

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      muslim |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |  -.0116593   .0198311    -0.59   0.557    -.0508237    .0275051
       _cons |    .225945     .02548     8.87   0.000     .1756245    .2762655
------------------------------------------------------------------------------


note: results saved to attrition.xml

. 
. foreach i in $demo3 {
  2. 
.         global list=""
  3. 
.         regress `i' drops2 if time==0, cluster(ea)
  4.         estimates store `i'_1
  5. 
.         global list="$list" + " `i'_1"
  6.         xml_tab $list, below save(attrition.xml) append sheet("drops `i'")
  7.         estimates clear
  8. 
. }

Linear regression                                      Number of obs =    1766
                                                       F(  1,   160) =    5.25
                                                       Prob > F      =  0.0232
                                                       R-squared     =  0.0032
                                                       Root MSE      =  .43337

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         job |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |   .0520373   .0227067     2.29   0.023     .0071937    .0968809
       _cons |    .233677   .0151782    15.40   0.000     .2037015    .2636524
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1766
                                                       F(  1,   160) =    0.21
                                                       Prob > F      =  0.6483
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .47191

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       agric |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |  -.0103863   .0227302    -0.46   0.648    -.0552762    .0345035
       _cons |   .3376289   .0222097    15.20   0.000     .2937669    .3814908
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1760
                                                       F(  1,   160) =    1.56
                                                       Prob > F      =  0.2141
                                                       R-squared     =  0.0010
                                                       Root MSE      =  .19677

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         com |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |  -.0129779   .0104037    -1.25   0.214    -.0335242    .0075684
       _cons |   .0447504   .0065223     6.86   0.000     .0318696    .0576313
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1760
                                                       F(  1,   160) =    0.07
                                                       Prob > F      =  0.7910
                                                       R-squared     =  0.0000
                                                       Root MSE      =   .2008

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         art |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |  -.0028955   .0109079    -0.27   0.791    -.0244375    .0186465
       _cons |   .0430293   .0060407     7.12   0.000     .0310994    .0549591
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1760
                                                       F(  1,   160) =    2.75
                                                       Prob > F      =  0.0992
                                                       R-squared     =  0.0014
                                                       Root MSE      =  .23683

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         man |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |   .0185501   .0111864     1.66   0.099     -.003542    .0406421
       _cons |   .0533563    .006544     8.15   0.000     .0404325      .06628
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1760
                                                       F(  1,   160) =    0.09
                                                       Prob > F      =  0.7589
                                                       R-squared     =  0.0001
                                                       Root MSE      =  .16131

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       assal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |   .0026105   .0084919     0.31   0.759    -.0141601    .0193812
       _cons |   .0258176   .0047314     5.46   0.000     .0164734    .0351617
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1760
                                                       F(  1,   160) =    0.89
                                                       Prob > F      =  0.3475
                                                       R-squared     =  0.0007
                                                       Root MSE      =  .22263

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         tea |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |   .0120079   .0127449     0.94   0.348    -.0131621    .0371779
       _cons |   .0481928    .007883     6.11   0.000     .0326247    .0637609
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1760
                                                       F(  1,   160) =    2.30
                                                       Prob > F      =  0.1316
                                                       R-squared     =  0.0010
                                                       Root MSE      =    .174

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      puboff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |  -.0118726   .0078328    -1.52   0.132    -.0273416    .0035964
       _cons |    .035284    .006238     5.66   0.000     .0229645    .0476035
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1760
                                                       F(  1,   160) =    0.28
                                                       Prob > F      =  0.5966
                                                       R-squared     =  0.0002
                                                       Root MSE      =  .18869

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        stud |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |  -.0052815   .0099596    -0.53   0.597    -.0249507    .0143877
       _cons |   .0387263   .0063985     6.05   0.000     .0260899    .0513628
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1766
                                                       F(  1,   160) =    2.11
                                                       Prob > F      =  0.1480
                                                       R-squared     =  0.0011
                                                       Root MSE      =  .33217

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         dom |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |  -.0227249   .0156343    -1.45   0.148    -.0536012    .0081514
       _cons |   .1340206   .0102704    13.05   0.000     .1137376    .1543037
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1764
                                                       F(  1,   160) =    3.35
                                                       Prob > F      =  0.0691
                                                       R-squared     =  0.0018
                                                       Root MSE      =  .36371

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
       house |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |  -.0322852   .0176438    -1.83   0.069      -.06713    .0025595
       _cons |   .8539519   .0103106    82.82   0.000     .8335894    .8743144
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1764
                                                       F(  1,   160) =    1.04
                                                       Prob > F      =  0.3090
                                                       R-squared     =  0.0006
                                                       Root MSE      =  .48888

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
        land |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |  -.0259278   .0254049    -1.02   0.309    -.0761001    .0242444
       _cons |   .6142612   .0200095    30.70   0.000     .5747444    .6537779
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1764
                                                       F(  1,   160) =    0.00
                                                       Prob > F      =  0.9506
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .43648

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
      cattle |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |    .001512    .024363     0.06   0.951    -.0466025    .0496266
       _cons |   .2551546   .0174233    14.64   0.000     .2207454    .2895639
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1764
                                                       F(  1,   160) =    0.01
                                                       Prob > F      =  0.9145
                                                       R-squared     =  0.0000
                                                       Root MSE      =  .44413

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
         cel |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |   .0022852   .0212627     0.11   0.915    -.0397066     .044277
       _cons |   .7293814    .022188    32.87   0.000     .6855624    .7732005
------------------------------------------------------------------------------


note: results saved to attrition.xml

Linear regression                                      Number of obs =    1676
                                                       F(  1,   160) =    0.90
                                                       Prob > F      =  0.3430
                                                       R-squared     =  0.0006
                                                       Root MSE      =  163.96

                                   (Std. Err. adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
             |               Robust
 expenditure |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      drops2 |   8.708547    9.15601     0.95   0.343    -9.373671    26.79076
       _cons |   125.7702   5.861513    21.46   0.000     114.1943    137.3462
------------------------------------------------------------------------------


note: results saved to attrition.xml

. 
. **********************************************
. *****  OA TABLE 15: MULTIPLE IMPUTATION  *****
. **********************************************
. 
. clear all

. set more off

. 
. use mozdata_aux, replace

. 
. drop if time==0
(1766 observations deleted)

. 
. keep tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 civiceduc hotl
> ine verdade pr1 pr2 pr3 time lazy control ea post post_miss health health_miss market market_m
> iss police police_miss sex age single divor school norelig protest relig com prof tea comform 
> dom econfood econmedic house oven lchang llomue lchuabo lchitewe lronga chitsua living

. sum tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 civiceduc hotli
> ne verdade pr1 pr2 pr3 time lazy control ea post post_miss health health_miss market market_mi
> ss police police_miss sex age single divor school norelig protest relig com prof tea comform d
> om econfood econmedic house oven lchang llomue lchuabo lchitewe lronga chitsua living

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tresp |      1121    .9134701    .2812704          0          1
     tfinger |      1121    .8483497    .3588417          0          1
       tseen |      1121    .2908118    .4543396          0          1
        intt |      1121    .8092265    .3177191          0          1
       carta |      1147     .179599    .3840205          0          1
-------------+--------------------------------------------------------
     guebas2 |      1031    .8457808     .361334          0          1
   dlakhama2 |      1031     .013579    .1157915          0          1
    simango2 |      1031     .028128    .1654188          0          1
    frelimo2 |      1048    .8530534    .3542217          0          1
     renamo2 |      1048    .0162214    .1263862          0          1
-------------+--------------------------------------------------------
   civiceduc |      1766    .2542469    .4355604          0          1
     hotline |      1766    .2468856    .4313218          0          1
     verdade |      1766    .2429219    .4289697          0          1
         pr1 |      1766    .2502831    .4332987          0          1
         pr2 |      1766    .2485844    .4323147          0          1
-------------+--------------------------------------------------------
         pr3 |      1766    .2468856    .4313218          0          1
        time |      1766           1           0          1          1
        lazy |      1766    .1426954     .349861          0          1
     control |      1766    .2559456     .436515          0          1
          ea |      1766    80.81257     46.5387          1        161
-------------+--------------------------------------------------------
        post |      1766    .1115515    .3149031          0          1
   post_miss |      1766    .0436014    .2042643          0          1
      health |      1766    .6189128    .4857915          0          1
 health_miss |      1766    .0441676    .2055255          0          1
      market |      1766    .7451869    .4358796          0          1
-------------+--------------------------------------------------------
 market_miss |      1766    .0492639    .2164797          0          1
      police |      1766    .2355606    .4244689          0          1
 police_miss |      1766    .0249151    .1559104          0          1
         sex |      1766    .4513024    .4977638          0          1
         age |      1750      37.596    13.58373         15         88
-------------+--------------------------------------------------------
      single |      1766    .1772367    .3819771          0          1
       divor |      1766    .0096263    .0976679          0          1
      school |      1763      2.4481    1.720544          0          8
     norelig |      1766    .0413364    .1991234          0          1
     protest |      1766    .3522084    .4777938          0          1
-------------+--------------------------------------------------------
       relig |      1759    3.734508    1.004261          1          5
         com |      1760    .0403409    .1968134          0          1
        prof |      1760    .0210227    .1435008          0          1
         tea |      1760    .0522727    .2226397          0          1
     comform |      1760    .0159091    .1251595          0          1
-------------+--------------------------------------------------------
         dom |      1766    .1262741    .3322521          0          1
    econfood |      1764    .9778912    1.155553          0          3
   econmedic |      1755    1.002279    1.165347          0          3
       house |      1764    .8429705    .3639317          0          1
        oven |      1764    .0793651    .2703844          0          1
-------------+--------------------------------------------------------
      lchang |      1765    .4713881    .4993222          0          1
      llomue |      1765    .1104816    .3135779          0          1
     lchuabo |      1765    .1269122    .3329689          0          1
    lchitewe |      1765    .0101983    .1004988          0          1
      lronga |      1765    .0906516    .2871943          0          1
-------------+--------------------------------------------------------
     chitsua |      1762    .0124858    .1110717          0          1
      living |      1761    3.177172    1.109546          0          4

. 
. *aggregate dummies
. 
. gen marital=single

. replace marital=2 if divor==1
(17 real changes made)

. 
. gen religion=norelig

. replace religion=2 if protest==1
(622 real changes made)

. 
. gen occupation=com
(6 missing values generated)

. replace occupation=2 if prof==1
(37 real changes made)

. replace occupation=3 if tea==1
(92 real changes made)

. replace occupation=4 if comform==1
(28 real changes made)

. replace occupation=5 if dom==1
(223 real changes made)

. 
. sum marital religion occupation

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     marital |      1766    .1964892    .4209918          0          2
    religion |      1766    .7457531    .9457953          0          2
  occupation |      1760    .9363636    1.772584          0          5

. 
. *imputation
. mi set mlong

. mi ice tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 pr1 pr2 pr3 
> post post_miss health health_miss market market_miss police police_miss sex age single divor s
> chool norelig protest relig com prof tea comform dom econfood econmedic house oven lchang llom
> ue lchuabo lchitewe lronga chitsua living marital religion occupation, add(10) seed(1139) eqdr
> op(sex: tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 marital rel
> igion occupation, age: tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 rena
> mo2 marital religion occupation, single: divor tresp tfinger tseen intt carta guebas2 dlakhama
> 2 simango2 frelimo2 renamo2 marital religion occupation, divor: single tresp tfinger tseen int
> t carta guebas2 dlakhama2 simango2 frelimo2 renamo2 marital religion occupation, marital: sing
> le divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 marital re
> ligion occupation, school: tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 
> renamo2 marital religion occupation, norelig: protest relig tresp tfinger tseen intt carta gue
> bas2 dlakhama2 simango2 frelimo2 renamo2 marital religion occupation, protest: norelig relig t
> resp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 marital religion occ
> upation, religion: norelig protest relig tresp tfinger tseen intt carta guebas2 dlakhama2 sima
> ngo2 frelimo2 renamo2 marital religion occupation, relig: norelig protest tresp tfinger tseen 
> intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 marital religion occupation, com: prof 
> tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 mar
> ital religion occupation, prof: com tea comform dom tresp tfinger tseen intt carta guebas2 dla
> khama2 simango2 frelimo2 renamo2 marital religion occupation, tea: com prof comform dom tresp 
> tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 marital religion occupati
> on, comform: com prof tea dom tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelim
> o2 renamo2 marital religion occupation, dom: com prof tea comform tresp tfinger tseen intt car
> ta guebas2 dlakhama2 simango2 frelimo2 renamo2 marital religion occupation, occupation: com pr
> of tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 
> marital religion occupation, econfood: econmedic tresp tfinger tseen intt carta guebas2 dlakha
> ma2 simango2 frelimo2 renamo2 marital religion occupation, econmedic: econfood tresp tfinger t
> seen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 marital religion occupation, house
> : oven tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 marital reli
> gion occupation, oven: house tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo
> 2 renamo2 marital religion occupation, lchang: llomue lchuabo lchitewe lronga tresp tfinger ts
> een intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 marital religion occupation, llomue
> : lchang lchuabo lchitewe lronga tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 fre
> limo2 renamo2 marital religion occupation, lchuabo: lchang llomue lchitewe lronga tresp tfinge
> r tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 marital religion occupation, lc
> hitewe: lchang llomue lchuabo lronga tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation, lronga: lchang llomue lchuabo lchitewe tresp tf
> inger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 marital religion occupation
> , chitsua: tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 marital 
> religion occupation, living: tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo
> 2 renamo2 marital religion occupation) eq(tresp: pr1 pr2 pr3 post post_miss health health_miss
>  sex age single divor protest com prof tea comform dom econfood house llomue chitsua living, t
> finger: pr1 pr2 pr3 post post_miss health health_miss sex age single divor protest com prof te
> a comform dom econfood house llomue chitsua living, tseen: pr1 pr2 pr3 post post_miss health h
> ealth_miss sex age single divor protest com prof tea comform dom econfood house llomue chitsua
>  living, intt: pr1 pr2 pr3 post post_miss health health_miss sex age single divor protest com 
> prof tea comform dom econfood house llomue chitsua living, carta: pr1 pr2 pr3 market market_mi
> ss sex age divor school protest relig com tea econfood econmedic chitsua, guebas2: pr1 pr2 pr3
>  post post_miss health health_miss police police_miss sex age single divor norelig protest com
>  prof comform econfood house oven lchang llomue lchuabo lchitewe lronga chitsua living, dlakha
> ma2: pr1 pr2 pr3 post post_miss health health_miss police police_miss sex age single divor nor
> elig protest com prof comform econfood house oven lchang llomue lchuabo lchitewe lronga chitsu
> a living, simango2: pr1 pr2 pr3 post post_miss health health_miss police police_miss sex age s
> ingle divor norelig protest com prof comform econfood house oven lchang llomue lchuabo lchitew
> e lronga chitsua living, frelimo2: pr1 pr2 pr3 post post_miss health health_miss police police
> _miss sex age single divor norelig protest com prof comform econfood house oven lchang llomue 
> lchuabo lchitewe lronga chitsua living, renamo2: pr1 pr2 pr3 post post_miss health health_miss
>  police police_miss sex age single divor norelig protest com prof comform econfood house oven 
> lchang llomue lchuabo lchitewe lronga chitsua living) cmd(marital religion occupation:mlogit, 
> school relig econfood econmedic living:ologit)  passive(com:occupation==1 \ prof:occupation==2
>  \ tea:occupation==3 \ comform:occupation==4 \ dom:occupation==5) substitute(occupation:com pr
> of tea comform dom) cycles(5)

   #missing |
     values |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        982       55.61       55.61
          1 |         15        0.85       56.46
          2 |          5        0.28       56.74
          3 |         19        1.08       57.81
          4 |         26        1.47       59.29
          5 |         97        5.49       64.78
          6 |          4        0.23       65.01
          9 |          1        0.06       65.06
         10 |        593       33.58       98.64
         11 |         19        1.08       99.72
         15 |          2        0.11       99.83
         16 |          1        0.06       99.89
         19 |          2        0.11      100.00
------------+-----------------------------------
      Total |      1,766      100.00

[ignoring eqdrop(sex:tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 
> marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(single:divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(divor:single tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(norelig:protest relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(protest:norelig relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(com:prof tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(prof:com tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(tea:com prof comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(comform:com prof tea dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(dom:com prof tea comform tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(marital:single divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 
> frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(religion:norelig protest relig tresp tfinger tseen intt carta guebas2 dlakhama2
>  simango2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

   Variable | Command | Prediction equation
------------+---------+-------------------------------------------------------
        pr1 |         | [No missing data in estimation sample]
        pr2 |         | [No missing data in estimation sample]
        pr3 |         | [No missing data in estimation sample]
       post |         | [No missing data in estimation sample]
  post_miss |         | [No missing data in estimation sample]
     health |         | [No missing data in estimation sample]
health_miss |         | [No missing data in estimation sample]
     market |         | [No missing data in estimation sample]
market_miss |         | [No missing data in estimation sample]
     police |         | [No missing data in estimation sample]
police_miss |         | [No missing data in estimation sample]
        sex |         | [No missing data in estimation sample]
     single |         | [No missing data in estimation sample]
      divor |         | [No missing data in estimation sample]
    norelig |         | [No missing data in estimation sample]
    protest |         | [No missing data in estimation sample]
    marital | mlogit  | [No missing data in estimation sample]
   religion | mlogit  | [No missing data in estimation sample]
     lchang | logit   | pr1 pr2 pr3 post post_miss health health_miss market
            |         | market_miss police police_miss sex age single divor
            |         | school norelig protest relig com prof tea comform dom
            |         | econfood econmedic house oven chitsua living
     llomue | logit   | pr1 pr2 pr3 post post_miss health health_miss market
            |         | market_miss police police_miss sex age single divor
            |         | school norelig protest relig com prof tea comform dom
            |         | econfood econmedic house oven chitsua living
    lchuabo | logit   | pr1 pr2 pr3 post post_miss health health_miss market
            |         | market_miss police police_miss sex age single divor
            |         | school norelig protest relig com prof tea comform dom
            |         | econfood econmedic house oven chitsua living
   lchitewe | logit   | pr1 pr2 pr3 post post_miss health health_miss market
            |         | market_miss police police_miss sex age single divor
            |         | school norelig protest relig com prof tea comform dom
            |         | econfood econmedic house oven chitsua living
     lronga | logit   | pr1 pr2 pr3 post post_miss health health_miss market
            |         | market_miss police police_miss sex age single divor
            |         | school norelig protest relig com prof tea comform dom
            |         | econfood econmedic house oven chitsua living
   econfood | ologit  | pr1 pr2 pr3 post post_miss health health_miss market
            |         | market_miss police police_miss sex age single divor
            |         | school norelig protest relig com prof tea comform dom
            |         | house oven lchang llomue lchuabo lchitewe lronga
            |         | chitsua living
      house | logit   | pr1 pr2 pr3 post post_miss health health_miss market
            |         | market_miss police police_miss sex age single divor
            |         | school norelig protest relig com prof tea comform dom
            |         | econfood econmedic lchang llomue lchuabo lchitewe
            |         | lronga chitsua living
       oven | logit   | pr1 pr2 pr3 post post_miss health health_miss market
            |         | market_miss police police_miss sex age single divor
            |         | school norelig protest relig com prof tea comform dom
            |         | econfood econmedic lchang llomue lchuabo lchitewe
            |         | lronga chitsua living
     school | ologit  | pr1 pr2 pr3 post post_miss health health_miss market
            |         | market_miss police police_miss sex age single divor
            |         | norelig protest relig com prof tea comform dom
            |         | econfood econmedic house oven lchang llomue lchuabo
            |         | lchitewe lronga chitsua living
    chitsua | logit   | pr1 pr2 pr3 post post_miss health health_miss market
            |         | market_miss police police_miss sex age single divor
            |         | school norelig protest relig com prof tea comform dom
            |         | econfood econmedic house oven lchang llomue lchuabo
            |         | lchitewe lronga living
     living | ologit  | pr1 pr2 pr3 post post_miss health health_miss market
            |         | market_miss police police_miss sex age single divor
            |         | school norelig protest relig com prof tea comform dom
            |         | econfood econmedic house oven lchang llomue lchuabo
            |         | lchitewe lronga chitsua
        com |         | [Passively imputed from occupation==1]
       prof |         | [Passively imputed from occupation==2]
        tea |         | [Passively imputed from occupation==3]
    comform |         | [Passively imputed from occupation==4]
        dom |         | [Passively imputed from occupation==5]
 occupation | mlogit  | pr1 pr2 pr3 post post_miss health health_miss market
            |         | market_miss police police_miss sex age single divor
            |         | school norelig protest relig econfood econmedic house
            |         | oven lchang llomue lchuabo lchitewe lronga chitsua
            |         | living
      relig | ologit  | pr1 pr2 pr3 post post_miss health health_miss market
            |         | market_miss police police_miss sex age single divor
            |         | school com prof tea comform dom econfood econmedic
            |         | house oven lchang llomue lchuabo lchitewe lronga
            |         | chitsua living
  econmedic | ologit  | pr1 pr2 pr3 post post_miss health health_miss market
            |         | market_miss police police_miss sex age single divor
            |         | school norelig protest relig com prof tea comform dom
            |         | house oven lchang llomue lchuabo lchitewe lronga
            |         | chitsua living
        age | regress | pr1 pr2 pr3 post post_miss health health_miss market
            |         | market_miss police police_miss sex single divor school
            |         | norelig protest relig com prof tea comform dom
            |         | econfood econmedic house oven lchang llomue lchuabo
            |         | lchitewe lronga chitsua living
      carta | logit   | pr1 pr2 pr3 market market_miss sex age divor school
            |         | protest relig com tea econfood econmedic chitsua
      tresp | logit   | pr1 pr2 pr3 post post_miss health health_miss sex age
            |         | single divor protest com prof tea comform dom econfood
            |         | house llomue chitsua living
    tfinger | logit   | pr1 pr2 pr3 post post_miss health health_miss sex age
            |         | single divor protest com prof tea comform dom econfood
            |         | house llomue chitsua living
      tseen | logit   | pr1 pr2 pr3 post post_miss health health_miss sex age
            |         | single divor protest com prof tea comform dom econfood
            |         | house llomue chitsua living
       intt | regress | pr1 pr2 pr3 post post_miss health health_miss sex age
            |         | single divor protest com prof tea comform dom econfood
            |         | house llomue chitsua living
   frelimo2 | logit   | pr1 pr2 pr3 post post_miss health health_miss police
            |         | police_miss sex age single divor norelig protest com
            |         | prof comform econfood house oven lchang llomue lchuabo
            |         | lchitewe lronga chitsua living
    renamo2 | logit   | pr1 pr2 pr3 post post_miss health health_miss police
            |         | police_miss sex age single divor norelig protest com
            |         | prof comform econfood house oven lchang llomue lchuabo
            |         | lchitewe lronga chitsua living
    guebas2 | logit   | pr1 pr2 pr3 post post_miss health health_miss police
            |         | police_miss sex age single divor norelig protest com
            |         | prof comform econfood house oven lchang llomue lchuabo
            |         | lchitewe lronga chitsua living
  dlakhama2 | logit   | pr1 pr2 pr3 post post_miss health health_miss police
            |         | police_miss sex age single divor norelig protest com
            |         | prof comform econfood house oven lchang llomue lchuabo
            |         | lchitewe lronga chitsua living
   simango2 | logit   | pr1 pr2 pr3 post post_miss health health_miss police
            |         | police_miss sex age single divor norelig protest com
            |         | prof comform econfood house oven lchang llomue lchuabo
            |         | lchitewe lronga chitsua living
------------------------------------------------------------------------------

Imputing 
[Perfect prediction detected: using auglogit to impute llomue]
.....1
[ignoring eqdrop(sex:tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 
> marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(single:divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(divor:single tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(norelig:protest relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(protest:norelig relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(com:prof tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(prof:com tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(tea:com prof comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(comform:com prof tea dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(dom:com prof tea comform tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(marital:single divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 
> frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(religion:norelig protest relig tresp tfinger tseen intt carta guebas2 dlakhama2
>  simango2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[Perfect prediction detected: using auglogit to impute llomue]
.....2
[ignoring eqdrop(sex:tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 
> marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(single:divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(divor:single tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(norelig:protest relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(protest:norelig relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(com:prof tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(prof:com tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(tea:com prof comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(comform:com prof tea dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(dom:com prof tea comform tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(marital:single divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 
> frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(religion:norelig protest relig tresp tfinger tseen intt carta guebas2 dlakhama2
>  simango2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[Perfect prediction detected: using auglogit to impute llomue]
.....3
[ignoring eqdrop(sex:tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 
> marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(single:divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(divor:single tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(norelig:protest relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(protest:norelig relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(com:prof tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(prof:com tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(tea:com prof comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(comform:com prof tea dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(dom:com prof tea comform tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(marital:single divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 
> frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(religion:norelig protest relig tresp tfinger tseen intt carta guebas2 dlakhama2
>  simango2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[Perfect prediction detected: using auglogit to impute llomue]
.....4
[ignoring eqdrop(sex:tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 
> marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(single:divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(divor:single tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(norelig:protest relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(protest:norelig relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(com:prof tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(prof:com tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(tea:com prof comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(comform:com prof tea dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(dom:com prof tea comform tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(marital:single divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 
> frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(religion:norelig protest relig tresp tfinger tseen intt carta guebas2 dlakhama2
>  simango2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[Perfect prediction detected: using auglogit to impute llomue]
.....5
[ignoring eqdrop(sex:tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 
> marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(single:divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(divor:single tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(norelig:protest relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(protest:norelig relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(com:prof tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(prof:com tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(tea:com prof comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(comform:com prof tea dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(dom:com prof tea comform tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(marital:single divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 
> frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(religion:norelig protest relig tresp tfinger tseen intt carta guebas2 dlakhama2
>  simango2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[Perfect prediction detected: using auglogit to impute llomue]
.....6
[ignoring eqdrop(sex:tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 
> marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(single:divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(divor:single tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(norelig:protest relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(protest:norelig relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(com:prof tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(prof:com tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(tea:com prof comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(comform:com prof tea dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(dom:com prof tea comform tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(marital:single divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 
> frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(religion:norelig protest relig tresp tfinger tseen intt carta guebas2 dlakhama2
>  simango2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[Perfect prediction detected: using auglogit to impute llomue]
.....7
[ignoring eqdrop(sex:tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 
> marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(single:divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(divor:single tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(norelig:protest relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(protest:norelig relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(com:prof tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(prof:com tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(tea:com prof comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(comform:com prof tea dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(dom:com prof tea comform tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(marital:single divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 
> frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(religion:norelig protest relig tresp tfinger tseen intt carta guebas2 dlakhama2
>  simango2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[Perfect prediction detected: using auglogit to impute llomue]
.....8
[ignoring eqdrop(sex:tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 
> marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(single:divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(divor:single tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(norelig:protest relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(protest:norelig relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(com:prof tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(prof:com tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(tea:com prof comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(comform:com prof tea dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(dom:com prof tea comform tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(marital:single divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 
> frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(religion:norelig protest relig tresp tfinger tseen intt carta guebas2 dlakhama2
>  simango2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[Perfect prediction detected: using auglogit to impute llomue]
.....9
[ignoring eqdrop(sex:tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2 
> marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(single:divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(divor:single tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2
>  renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(norelig:protest relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(protest:norelig relig tresp tfinger tseen intt carta guebas2 dlakhama2 simango2
>  frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(com:prof tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(prof:com tea comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(tea:com prof comform dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(comform:com prof tea dom tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(dom:com prof tea comform tresp tfinger tseen intt carta guebas2 dlakhama2 siman
> go2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(marital:single divor tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 
> frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[ignoring eqdrop(religion:norelig protest relig tresp tfinger tseen intt carta guebas2 dlakhama2
>  simango2 frelimo2 renamo2 marital religion occupation), no equation needed for occupation]

[Perfect prediction detected: using auglogit to impute llomue]
.....10
file /var/folders/bq/82fvg0l108qfhv9c2v8t41t40000gn/T//S_02237.000001 saved
(10 imputations added; M=10)

. 
. mi xtset, clear

. 
. *estimation
. 
. *targeted and untargeted together
. 
. global list1=""

. global list2=""

. 
. mi estimate, dots: regress tresp civiceduc hotline verdade pr1 pr2 pr3 post post_miss health h
> ealth_miss sex age single divor protest com prof tea comform dom econfood house llomue chitsua
>  living, cluster(ea)

Imputations (10):
  .........10 done

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     1.0331
                                                  Largest FMI     =     0.8729
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =       8.25
                                                          avg     =      41.11
                                                          max     =      92.80
Model F test:       Equal FMI                     F(  25,  127.3) =       1.27
Within VCE type:       Robust                     Prob > F        =     0.1982

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       tresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0218454   .0219964     0.99   0.324    -.0219493    .0656402
     hotline |   .0359251   .0261778     1.37   0.181    -.0178332    .0896835
     verdade |   .0152007   .0256044     0.59   0.556    -.0363178    .0667192
         pr1 |  -.0454345   .0299518    -1.52   0.136     -.105723    .0148541
         pr2 |  -.0080576   .0253037    -0.32   0.751    -.0584581     .042343
         pr3 |  -.0033324   .0232674    -0.14   0.887    -.0496991    .0430343
        post |  -.0557659     .02987    -1.87   0.067    -.1155775    .0040458
   post_miss |   .0029376   .0407651     0.07   0.943    -.0787692    .0846444
      health |   .0014157   .0214476     0.07   0.948    -.0429732    .0458046
 health_miss |  -.0150225    .045194    -0.33   0.741    -.1062015    .0761565
         sex |    .041421   .0172012     2.41   0.021     .0066862    .0761559
         age |  -.0013421   .0007527    -1.78   0.082    -.0028645    .0001804
      single |   -.052766   .0281488    -1.87   0.069    -.1098911     .004359
       divor |  -.0108177   .1096484    -0.10   0.922    -.2374674     .215832
     protest |  -.0003727   .0235971    -0.02   0.988    -.0487267    .0479812
         com |  -.0458409   .0545086    -0.84   0.409    -.1588834    .0672016
        prof |   .0820572   .0641438     1.28   0.236    -.0650865    .2292008
         tea |   .0039465    .044408     0.09   0.930    -.0866152    .0945082
     comform |  -.1907426   .1111166    -1.72   0.099    -.4203457    .0388606
         dom |  -.0587966   .0295331    -1.99   0.049    -.1174451   -.0001481
    econfood |  -.0125139   .0091045    -1.37   0.183    -.0313608     .006333
       house |  -.0013668   .0256142    -0.05   0.958    -.0534001    .0506665
      llomue |  -.0394002   .0461363    -0.85   0.400    -.1335567    .0547562
     chitsua |  -.0372094   .0857528    -0.43   0.667    -.2123982    .1379795
      living |    .029666   .0100732     2.95   0.005     .0093967    .0499354
       _cons |   .8866916   .0470894    18.83   0.000     .7913295    .9820538
------------------------------------------------------------------------------

. estimates store tresp

. 
. mi estimate, dots: mean tresp if control==1

Imputations (10):
  .........10 done

Multiple-imputation estimates      Imputations     =        10
Mean estimation                    Number of obs   =       452
                                   Average RVI     =    0.1640
                                   Largest FMI     =    0.1452
                                   Complete DF     =       451
DF adjustment:   Small sample      DF:     min     =    208.45
                                           avg     =    208.45
Within VCE type:     Analytic              max     =    208.45

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       tresp |   .8880531   .0160156      .8564798    .9196264
--------------------------------------------------------------

. matrix define aux=e(b_mi)

. scalar define m_tresp=aux[1,1]

. display m_tresp
.8880531

. 
. mi estimate (diff: _b[civiceduc]-_b[hotline]), saving(miest, replace): regress tresp civiceduc
>  hotline verdade pr1 pr2 pr3 post post_miss health health_miss sex age single divor protest co
> m prof tea comform dom econfood house llomue chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     1.0331
                                                  Largest FMI     =     0.8729
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =       8.25
                                                          avg     =      41.11
                                                          max     =      92.80
Model F test:       Equal FMI                     F(  25,  127.3) =       1.27
Within VCE type:       Robust                     Prob > F        =     0.1982

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       tresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0218454   .0219964     0.99   0.324    -.0219493    .0656402
     hotline |   .0359251   .0261778     1.37   0.181    -.0178332    .0896835
     verdade |   .0152007   .0256044     0.59   0.556    -.0363178    .0667192
         pr1 |  -.0454345   .0299518    -1.52   0.136     -.105723    .0148541
         pr2 |  -.0080576   .0253037    -0.32   0.751    -.0584581     .042343
         pr3 |  -.0033324   .0232674    -0.14   0.887    -.0496991    .0430343
        post |  -.0557659     .02987    -1.87   0.067    -.1155775    .0040458
   post_miss |   .0029376   .0407651     0.07   0.943    -.0787692    .0846444
      health |   .0014157   .0214476     0.07   0.948    -.0429732    .0458046
 health_miss |  -.0150225    .045194    -0.33   0.741    -.1062015    .0761565
         sex |    .041421   .0172012     2.41   0.021     .0066862    .0761559
         age |  -.0013421   .0007527    -1.78   0.082    -.0028645    .0001804
      single |   -.052766   .0281488    -1.87   0.069    -.1098911     .004359
       divor |  -.0108177   .1096484    -0.10   0.922    -.2374674     .215832
     protest |  -.0003727   .0235971    -0.02   0.988    -.0487267    .0479812
         com |  -.0458409   .0545086    -0.84   0.409    -.1588834    .0672016
        prof |   .0820572   .0641438     1.28   0.236    -.0650865    .2292008
         tea |   .0039465    .044408     0.09   0.930    -.0866152    .0945082
     comform |  -.1907426   .1111166    -1.72   0.099    -.4203457    .0388606
         dom |  -.0587966   .0295331    -1.99   0.049    -.1174451   -.0001481
    econfood |  -.0125139   .0091045    -1.37   0.183    -.0313608     .006333
       house |  -.0013668   .0256142    -0.05   0.958    -.0534001    .0506665
      llomue |  -.0394002   .0461363    -0.85   0.400    -.1335567    .0547562
     chitsua |  -.0372094   .0857528    -0.43   0.667    -.2123982    .1379795
      living |    .029666   .0100732     2.95   0.005     .0093967    .0499354
       _cons |   .8866916   .0470894    18.83   0.000     .7913295    .9820538
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.7671
                                                  Largest FMI     =     0.4606
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      31.13
                                                          avg     =      31.13
Within VCE type:       Robust                             max     =      31.13

         diff: _b[civiceduc]-_b[hotline]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       tresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |  -.0140797   .0229985    -0.61   0.545    -.0609773    .0328179
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[hotline]

 ( 1)  diff = 0

       F(  1,  31.1) =    0.37
            Prob > F =    0.5448

. scalar define t1_tresp=r(p)

. display t1_tresp
.5448499

. 
. mi estimate (diff: _b[civiceduc]-_b[verdade]), saving(miest, replace): regress tresp civiceduc
>  hotline verdade pr1 pr2 pr3 post post_miss health health_miss sex age single divor protest co
> m prof tea comform dom econfood house llomue chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     1.0331
                                                  Largest FMI     =     0.8729
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =       8.25
                                                          avg     =      41.11
                                                          max     =      92.80
Model F test:       Equal FMI                     F(  25,  127.3) =       1.27
Within VCE type:       Robust                     Prob > F        =     0.1982

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       tresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0218454   .0219964     0.99   0.324    -.0219493    .0656402
     hotline |   .0359251   .0261778     1.37   0.181    -.0178332    .0896835
     verdade |   .0152007   .0256044     0.59   0.556    -.0363178    .0667192
         pr1 |  -.0454345   .0299518    -1.52   0.136     -.105723    .0148541
         pr2 |  -.0080576   .0253037    -0.32   0.751    -.0584581     .042343
         pr3 |  -.0033324   .0232674    -0.14   0.887    -.0496991    .0430343
        post |  -.0557659     .02987    -1.87   0.067    -.1155775    .0040458
   post_miss |   .0029376   .0407651     0.07   0.943    -.0787692    .0846444
      health |   .0014157   .0214476     0.07   0.948    -.0429732    .0458046
 health_miss |  -.0150225    .045194    -0.33   0.741    -.1062015    .0761565
         sex |    .041421   .0172012     2.41   0.021     .0066862    .0761559
         age |  -.0013421   .0007527    -1.78   0.082    -.0028645    .0001804
      single |   -.052766   .0281488    -1.87   0.069    -.1098911     .004359
       divor |  -.0108177   .1096484    -0.10   0.922    -.2374674     .215832
     protest |  -.0003727   .0235971    -0.02   0.988    -.0487267    .0479812
         com |  -.0458409   .0545086    -0.84   0.409    -.1588834    .0672016
        prof |   .0820572   .0641438     1.28   0.236    -.0650865    .2292008
         tea |   .0039465    .044408     0.09   0.930    -.0866152    .0945082
     comform |  -.1907426   .1111166    -1.72   0.099    -.4203457    .0388606
         dom |  -.0587966   .0295331    -1.99   0.049    -.1174451   -.0001481
    econfood |  -.0125139   .0091045    -1.37   0.183    -.0313608     .006333
       house |  -.0013668   .0256142    -0.05   0.958    -.0534001    .0506665
      llomue |  -.0394002   .0461363    -0.85   0.400    -.1335567    .0547562
     chitsua |  -.0372094   .0857528    -0.43   0.667    -.2123982    .1379795
      living |    .029666   .0100732     2.95   0.005     .0093967    .0499354
       _cons |   .8866916   .0470894    18.83   0.000     .7913295    .9820538
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.5050
                                                  Largest FMI     =     0.3551
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      45.38
                                                          avg     =      45.38
Within VCE type:       Robust                             max     =      45.38

         diff: _b[civiceduc]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       tresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |   .0066447    .024649     0.27   0.789    -.0429893    .0562788
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[verdade]

 ( 1)  diff = 0

       F(  1,  45.4) =    0.07
            Prob > F =    0.7887

. scalar define t2_tresp=r(p)

. display t2_tresp
.78870971

. 
. mi estimate (diff: _b[hotline]-_b[verdade]), saving(miest, replace): regress tresp civiceduc h
> otline verdade pr1 pr2 pr3 post post_miss health health_miss sex age single divor protest com 
> prof tea comform dom econfood house llomue chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     1.0331
                                                  Largest FMI     =     0.8729
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =       8.25
                                                          avg     =      41.11
                                                          max     =      92.80
Model F test:       Equal FMI                     F(  25,  127.3) =       1.27
Within VCE type:       Robust                     Prob > F        =     0.1982

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       tresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0218454   .0219964     0.99   0.324    -.0219493    .0656402
     hotline |   .0359251   .0261778     1.37   0.181    -.0178332    .0896835
     verdade |   .0152007   .0256044     0.59   0.556    -.0363178    .0667192
         pr1 |  -.0454345   .0299518    -1.52   0.136     -.105723    .0148541
         pr2 |  -.0080576   .0253037    -0.32   0.751    -.0584581     .042343
         pr3 |  -.0033324   .0232674    -0.14   0.887    -.0496991    .0430343
        post |  -.0557659     .02987    -1.87   0.067    -.1155775    .0040458
   post_miss |   .0029376   .0407651     0.07   0.943    -.0787692    .0846444
      health |   .0014157   .0214476     0.07   0.948    -.0429732    .0458046
 health_miss |  -.0150225    .045194    -0.33   0.741    -.1062015    .0761565
         sex |    .041421   .0172012     2.41   0.021     .0066862    .0761559
         age |  -.0013421   .0007527    -1.78   0.082    -.0028645    .0001804
      single |   -.052766   .0281488    -1.87   0.069    -.1098911     .004359
       divor |  -.0108177   .1096484    -0.10   0.922    -.2374674     .215832
     protest |  -.0003727   .0235971    -0.02   0.988    -.0487267    .0479812
         com |  -.0458409   .0545086    -0.84   0.409    -.1588834    .0672016
        prof |   .0820572   .0641438     1.28   0.236    -.0650865    .2292008
         tea |   .0039465    .044408     0.09   0.930    -.0866152    .0945082
     comform |  -.1907426   .1111166    -1.72   0.099    -.4203457    .0388606
         dom |  -.0587966   .0295331    -1.99   0.049    -.1174451   -.0001481
    econfood |  -.0125139   .0091045    -1.37   0.183    -.0313608     .006333
       house |  -.0013668   .0256142    -0.05   0.958    -.0534001    .0506665
      llomue |  -.0394002   .0461363    -0.85   0.400    -.1335567    .0547562
     chitsua |  -.0372094   .0857528    -0.43   0.667    -.2123982    .1379795
      living |    .029666   .0100732     2.95   0.005     .0093967    .0499354
       _cons |   .8866916   .0470894    18.83   0.000     .7913295    .9820538
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.3488
                                                  Largest FMI     =     0.2722
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      62.64
                                                          avg     =      62.64
Within VCE type:       Robust                             max     =      62.64

         diff: _b[hotline]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       tresp |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |   .0207245   .0220711     0.94   0.351    -.0233861    .0648351
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[hotline]-_b[verdade]

 ( 1)  diff = 0

       F(  1,  62.6) =    0.88
            Prob > F =    0.3513

. scalar define t3_tresp=r(p)

. display t3_tresp
.35134524

. 
. mi test civiceduc hotline verdade
note: assuming equal fractions of missing information

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,  76.8) =    0.81
            Prob > F =    0.4943

. scalar define t4_tresp=r(p)

. display t4_tresp
.4943356

. 
. mi estimate, dots: regress tfinger civiceduc hotline verdade pr1 pr2 pr3 post post_miss health
>  health_miss sex age single divor protest com prof tea comform dom econfood house llomue chits
> ua living, cluster(ea)

Imputations (10):
  .........10 done

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.9002
                                                  Largest FMI     =     0.6675
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      16.17
                                                          avg     =      36.88
                                                          max     =      84.33
Model F test:       Equal FMI                     F(  25,  131.1) =       2.53
Within VCE type:       Robust                     Prob > F        =     0.0004

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     tfinger |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0431081    .026165     1.65   0.107     -.009621    .0958372
     hotline |   .0348897   .0284052     1.23   0.230    -.0233939    .0931734
     verdade |    .022624   .0322855     0.70   0.488    -.0429449    .0881929
         pr1 |   .0213989    .043587     0.49   0.630    -.0709208    .1137185
         pr2 |  -.0029607   .0414722    -0.07   0.944    -.0894623     .083541
         pr3 |   .0611912   .0311153     1.97   0.057    -.0018755    .1242578
        post |  -.0609717   .0378139    -1.61   0.122    -.1396345    .0176912
   post_miss |  -.0079539   .0514794    -0.15   0.878    -.1109076    .0949999
      health |   .0311654    .025614     1.22   0.233     -.021044    .0833748
 health_miss |   .0174044   .0696075     0.25   0.805    -.1272379    .1620468
         sex |   .0525085   .0192563     2.73   0.008     .0142174    .0907995
         age |  -.0003996   .0009458    -0.42   0.675    -.0023178    .0015187
      single |  -.0530922    .034336    -1.55   0.133    -.1232823    .0170979
       divor |  -.0036326   .0971964    -0.04   0.970    -.1977399    .1904746
     protest |    .027452   .0303397     0.90   0.376    -.0356403    .0905442
         com |  -.0196914   .0504996    -0.39   0.698    -.1204844    .0811016
        prof |   .1447308   .0428264     3.38   0.002     .0564417    .2330199
         tea |  -.0155675   .0488566    -0.32   0.752    -.1144868    .0833517
     comform |  -.2598923   .1185815    -2.19   0.037     -.503113   -.0166715
         dom |   .0020741   .0354752     0.06   0.954    -.0688048     .072953
    econfood |  -.0078786    .009775    -0.81   0.425    -.0276622    .0119051
       house |   .0222445   .0379804     0.59   0.564    -.0566391    .1011281
      llomue |  -.0623744   .0594893    -1.05   0.304    -.1848678     .060119
     chitsua |  -.0331123   .1019868    -0.32   0.747    -.2381479    .1719233
      living |   .0375457   .0123646     3.04   0.004     .0125494    .0625421
       _cons |   .6575314    .074969     8.77   0.000     .4996204    .8154424
------------------------------------------------------------------------------

. estimates store tfinger

. 
. mi estimate, dots: mean tfinger if control==1

Imputations (10):
  .........10 done

Multiple-imputation estimates      Imputations     =        10
Mean estimation                    Number of obs   =       452
                                   Average RVI     =    0.4942
                                   Largest FMI     =    0.3477
                                   Complete DF     =       451
DF adjustment:   Small sample      DF:     min     =     64.58
                                           avg     =     64.58
Within VCE type:     Analytic              max     =     64.58

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
     tfinger |   .8196903   .0221188      .7755106      .86387
--------------------------------------------------------------

. matrix define aux=e(b_mi)

. scalar define m_tfinger=aux[1,1]

. display m_tfinger
.81969027

. 
. mi estimate (diff: _b[civiceduc]-_b[hotline]), saving(miest, replace): regress tfinger civiced
> uc hotline verdade pr1 pr2 pr3 post post_miss health health_miss sex age single divor protest 
> com prof tea comform dom econfood house llomue chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.9002
                                                  Largest FMI     =     0.6675
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      16.17
                                                          avg     =      36.88
                                                          max     =      84.33
Model F test:       Equal FMI                     F(  25,  131.1) =       2.53
Within VCE type:       Robust                     Prob > F        =     0.0004

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     tfinger |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0431081    .026165     1.65   0.107     -.009621    .0958372
     hotline |   .0348897   .0284052     1.23   0.230    -.0233939    .0931734
     verdade |    .022624   .0322855     0.70   0.488    -.0429449    .0881929
         pr1 |   .0213989    .043587     0.49   0.630    -.0709208    .1137185
         pr2 |  -.0029607   .0414722    -0.07   0.944    -.0894623     .083541
         pr3 |   .0611912   .0311153     1.97   0.057    -.0018755    .1242578
        post |  -.0609717   .0378139    -1.61   0.122    -.1396345    .0176912
   post_miss |  -.0079539   .0514794    -0.15   0.878    -.1109076    .0949999
      health |   .0311654    .025614     1.22   0.233     -.021044    .0833748
 health_miss |   .0174044   .0696075     0.25   0.805    -.1272379    .1620468
         sex |   .0525085   .0192563     2.73   0.008     .0142174    .0907995
         age |  -.0003996   .0009458    -0.42   0.675    -.0023178    .0015187
      single |  -.0530922    .034336    -1.55   0.133    -.1232823    .0170979
       divor |  -.0036326   .0971964    -0.04   0.970    -.1977399    .1904746
     protest |    .027452   .0303397     0.90   0.376    -.0356403    .0905442
         com |  -.0196914   .0504996    -0.39   0.698    -.1204844    .0811016
        prof |   .1447308   .0428264     3.38   0.002     .0564417    .2330199
         tea |  -.0155675   .0488566    -0.32   0.752    -.1144868    .0833517
     comform |  -.2598923   .1185815    -2.19   0.037     -.503113   -.0166715
         dom |   .0020741   .0354752     0.06   0.954    -.0688048     .072953
    econfood |  -.0078786    .009775    -0.81   0.425    -.0276622    .0119051
       house |   .0222445   .0379804     0.59   0.564    -.0566391    .1011281
      llomue |  -.0623744   .0594893    -1.05   0.304    -.1848678     .060119
     chitsua |  -.0331123   .1019868    -0.32   0.747    -.2381479    .1719233
      living |   .0375457   .0123646     3.04   0.004     .0125494    .0625421
       _cons |   .6575314    .074969     8.77   0.000     .4996204    .8154424
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.2049
                                                  Largest FMI     =     0.1774
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      92.26
                                                          avg     =      92.26
Within VCE type:       Robust                             max     =      92.26

         diff: _b[civiceduc]-_b[hotline]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     tfinger |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |   .0082184    .022593     0.36   0.717    -.0366516    .0530884
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[hotline]

 ( 1)  diff = 0

       F(  1,  92.3) =    0.13
            Prob > F =    0.7169

. scalar define t1_tfinger=r(p)

. display t1_tfinger
.71687163

. 
. mi estimate (diff: _b[civiceduc]-_b[verdade]), saving(miest, replace): regress tfinger civiced
> uc hotline verdade pr1 pr2 pr3 post post_miss health health_miss sex age single divor protest 
> com prof tea comform dom econfood house llomue chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.9002
                                                  Largest FMI     =     0.6675
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      16.17
                                                          avg     =      36.88
                                                          max     =      84.33
Model F test:       Equal FMI                     F(  25,  131.1) =       2.53
Within VCE type:       Robust                     Prob > F        =     0.0004

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     tfinger |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0431081    .026165     1.65   0.107     -.009621    .0958372
     hotline |   .0348897   .0284052     1.23   0.230    -.0233939    .0931734
     verdade |    .022624   .0322855     0.70   0.488    -.0429449    .0881929
         pr1 |   .0213989    .043587     0.49   0.630    -.0709208    .1137185
         pr2 |  -.0029607   .0414722    -0.07   0.944    -.0894623     .083541
         pr3 |   .0611912   .0311153     1.97   0.057    -.0018755    .1242578
        post |  -.0609717   .0378139    -1.61   0.122    -.1396345    .0176912
   post_miss |  -.0079539   .0514794    -0.15   0.878    -.1109076    .0949999
      health |   .0311654    .025614     1.22   0.233     -.021044    .0833748
 health_miss |   .0174044   .0696075     0.25   0.805    -.1272379    .1620468
         sex |   .0525085   .0192563     2.73   0.008     .0142174    .0907995
         age |  -.0003996   .0009458    -0.42   0.675    -.0023178    .0015187
      single |  -.0530922    .034336    -1.55   0.133    -.1232823    .0170979
       divor |  -.0036326   .0971964    -0.04   0.970    -.1977399    .1904746
     protest |    .027452   .0303397     0.90   0.376    -.0356403    .0905442
         com |  -.0196914   .0504996    -0.39   0.698    -.1204844    .0811016
        prof |   .1447308   .0428264     3.38   0.002     .0564417    .2330199
         tea |  -.0155675   .0488566    -0.32   0.752    -.1144868    .0833517
     comform |  -.2598923   .1185815    -2.19   0.037     -.503113   -.0166715
         dom |   .0020741   .0354752     0.06   0.954    -.0688048     .072953
    econfood |  -.0078786    .009775    -0.81   0.425    -.0276622    .0119051
       house |   .0222445   .0379804     0.59   0.564    -.0566391    .1011281
      llomue |  -.0623744   .0594893    -1.05   0.304    -.1848678     .060119
     chitsua |  -.0331123   .1019868    -0.32   0.747    -.2381479    .1719233
      living |   .0375457   .0123646     3.04   0.004     .0125494    .0625421
       _cons |   .6575314    .074969     8.77   0.000     .4996204    .8154424
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.2066
                                                  Largest FMI     =     0.1786
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      91.82
                                                          avg     =      91.82
Within VCE type:       Robust                             max     =      91.82

         diff: _b[civiceduc]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     tfinger |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |   .0204841   .0278521     0.74   0.464     -.034834    .0758021
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[verdade]

 ( 1)  diff = 0

       F(  1,  91.8) =    0.54
            Prob > F =    0.4639

. scalar define t2_tfinger=r(p)

. display t2_tfinger
.46393368

. 
. mi estimate (diff: _b[hotline]-_b[verdade]), saving(miest, replace): regress tfinger civiceduc
>  hotline verdade pr1 pr2 pr3 post post_miss health health_miss sex age single divor protest co
> m prof tea comform dom econfood house llomue chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.9002
                                                  Largest FMI     =     0.6675
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      16.17
                                                          avg     =      36.88
                                                          max     =      84.33
Model F test:       Equal FMI                     F(  25,  131.1) =       2.53
Within VCE type:       Robust                     Prob > F        =     0.0004

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     tfinger |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0431081    .026165     1.65   0.107     -.009621    .0958372
     hotline |   .0348897   .0284052     1.23   0.230    -.0233939    .0931734
     verdade |    .022624   .0322855     0.70   0.488    -.0429449    .0881929
         pr1 |   .0213989    .043587     0.49   0.630    -.0709208    .1137185
         pr2 |  -.0029607   .0414722    -0.07   0.944    -.0894623     .083541
         pr3 |   .0611912   .0311153     1.97   0.057    -.0018755    .1242578
        post |  -.0609717   .0378139    -1.61   0.122    -.1396345    .0176912
   post_miss |  -.0079539   .0514794    -0.15   0.878    -.1109076    .0949999
      health |   .0311654    .025614     1.22   0.233     -.021044    .0833748
 health_miss |   .0174044   .0696075     0.25   0.805    -.1272379    .1620468
         sex |   .0525085   .0192563     2.73   0.008     .0142174    .0907995
         age |  -.0003996   .0009458    -0.42   0.675    -.0023178    .0015187
      single |  -.0530922    .034336    -1.55   0.133    -.1232823    .0170979
       divor |  -.0036326   .0971964    -0.04   0.970    -.1977399    .1904746
     protest |    .027452   .0303397     0.90   0.376    -.0356403    .0905442
         com |  -.0196914   .0504996    -0.39   0.698    -.1204844    .0811016
        prof |   .1447308   .0428264     3.38   0.002     .0564417    .2330199
         tea |  -.0155675   .0488566    -0.32   0.752    -.1144868    .0833517
     comform |  -.2598923   .1185815    -2.19   0.037     -.503113   -.0166715
         dom |   .0020741   .0354752     0.06   0.954    -.0688048     .072953
    econfood |  -.0078786    .009775    -0.81   0.425    -.0276622    .0119051
       house |   .0222445   .0379804     0.59   0.564    -.0566391    .1011281
      llomue |  -.0623744   .0594893    -1.05   0.304    -.1848678     .060119
     chitsua |  -.0331123   .1019868    -0.32   0.747    -.2381479    .1719233
      living |   .0375457   .0123646     3.04   0.004     .0125494    .0625421
       _cons |   .6575314    .074969     8.77   0.000     .4996204    .8154424
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.1051
                                                  Largest FMI     =     0.0982
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =     125.05
                                                          avg     =     125.05
Within VCE type:       Robust                             max     =     125.05

         diff: _b[hotline]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     tfinger |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |   .0122657   .0256655     0.48   0.634    -.0385294    .0630608
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[hotline]-_b[verdade]

 ( 1)  diff = 0

       F(  1, 125.0) =    0.23
            Prob > F =    0.6336

. scalar define t3_tfinger=r(p)

. display t3_tfinger
.63355174

. 
. mi test civiceduc hotline verdade
note: assuming equal fractions of missing information

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,  88.3) =    1.08
            Prob > F =    0.3597

. scalar define t4_tfinger=r(p)

. display t4_tfinger
.35974875

. 
. mi estimate, dots: regress tseen civiceduc hotline verdade pr1 pr2 pr3 post post_miss health h
> ealth_miss sex age single divor protest com prof tea comform dom econfood house llomue chitsua
>  living, cluster(ea)

Imputations (10):
  .........10 done

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.8028
                                                  Largest FMI     =     0.6468
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      17.22
                                                          avg     =      38.89
                                                          max     =      92.29
Model F test:       Equal FMI                     F(  25,  133.9) =       3.84
Within VCE type:       Robust                     Prob > F        =     0.0000

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0353357   .0367192     0.96   0.343    -.0394011    .1100724
     hotline |   .0397137   .0355022     1.12   0.269    -.0317391    .1111665
     verdade |   .0671563   .0379074     1.77   0.083    -.0089946    .1433072
         pr1 |   .1623704   .0425134     3.82   0.001     .0752633    .2494775
         pr2 |   .1673535   .0425797     3.93   0.000     .0822704    .2524366
         pr3 |   .2257457   .0432683     5.22   0.000     .1384946    .3129967
        post |  -.0369107   .0403608    -0.91   0.363    -.1170673    .0432459
   post_miss |  -.0501368   .0599875    -0.84   0.415    -.1765778    .0763042
      health |    .046196   .0360693     1.28   0.211    -.0277688    .1201607
 health_miss |   .0778251    .057602     1.35   0.192    -.0424113    .1980616
         sex |   .0541128   .0293608     1.84   0.073    -.0052222    .1134478
         age |   .0032687   .0011145     2.93   0.005     .0010314     .005506
      single |  -.0303396   .0390206    -0.78   0.445    -.1111442     .050465
       divor |  -.1927525   .1341667    -1.44   0.165    -.4711947    .0856898
     protest |   .0266569   .0364904     0.73   0.472    -.0484373    .1017512
         com |  -.0131006   .0658885    -0.20   0.843    -.1450197    .1188185
        prof |   .2686944   .1132802     2.37   0.024     .0383348     .499054
         tea |   .0535362   .0611395     0.88   0.385    -.0692975    .1763698
     comform |   -.029029   .1203844    -0.24   0.812    -.2789818    .2209238
         dom |   .0246562   .0372426     0.66   0.510    -.0496861    .0989984
    econfood |  -.0015741   .0142529    -0.11   0.913    -.0308924    .0277442
       house |   .0213515   .0394722     0.54   0.593    -.0596929    .1023959
      llomue |  -.0471791   .0576122    -0.82   0.421    -.1661172     .071759
     chitsua |  -.0965657   .1155646    -0.84   0.407    -.3286638    .1355323
      living |   .0127559   .0123514     1.03   0.309    -.0123192    .0378309
       _cons |  -.1117456   .0720209    -1.55   0.128    -.2572067    .0337155
------------------------------------------------------------------------------

. estimates store tseen

. 
. mi estimate, dots: mean tseen if control==1

Imputations (10):
  .........10 done

Multiple-imputation estimates      Imputations     =        10
Mean estimation                    Number of obs   =       452
                                   Average RVI     =    0.4930
                                   Largest FMI     =    0.3471
                                   Complete DF     =       451
DF adjustment:   Small sample      DF:     min     =     64.77
                                           avg     =     64.77
Within VCE type:     Analytic              max     =     64.77

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       tseen |   .2654867   .0253962      .2147636    .3162099
--------------------------------------------------------------

. matrix define aux=e(b_mi)

. scalar define m_tseen=aux[1,1]

. display m_tseen
.26548673

. 
. mi estimate (diff: _b[civiceduc]-_b[hotline]), saving(miest, replace): regress tseen civiceduc
>  hotline verdade pr1 pr2 pr3 post post_miss health health_miss sex age single divor protest co
> m prof tea comform dom econfood house llomue chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.8028
                                                  Largest FMI     =     0.6468
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      17.22
                                                          avg     =      38.89
                                                          max     =      92.29
Model F test:       Equal FMI                     F(  25,  133.9) =       3.84
Within VCE type:       Robust                     Prob > F        =     0.0000

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0353357   .0367192     0.96   0.343    -.0394011    .1100724
     hotline |   .0397137   .0355022     1.12   0.269    -.0317391    .1111665
     verdade |   .0671563   .0379074     1.77   0.083    -.0089946    .1433072
         pr1 |   .1623704   .0425134     3.82   0.001     .0752633    .2494775
         pr2 |   .1673535   .0425797     3.93   0.000     .0822704    .2524366
         pr3 |   .2257457   .0432683     5.22   0.000     .1384946    .3129967
        post |  -.0369107   .0403608    -0.91   0.363    -.1170673    .0432459
   post_miss |  -.0501368   .0599875    -0.84   0.415    -.1765778    .0763042
      health |    .046196   .0360693     1.28   0.211    -.0277688    .1201607
 health_miss |   .0778251    .057602     1.35   0.192    -.0424113    .1980616
         sex |   .0541128   .0293608     1.84   0.073    -.0052222    .1134478
         age |   .0032687   .0011145     2.93   0.005     .0010314     .005506
      single |  -.0303396   .0390206    -0.78   0.445    -.1111442     .050465
       divor |  -.1927525   .1341667    -1.44   0.165    -.4711947    .0856898
     protest |   .0266569   .0364904     0.73   0.472    -.0484373    .1017512
         com |  -.0131006   .0658885    -0.20   0.843    -.1450197    .1188185
        prof |   .2686944   .1132802     2.37   0.024     .0383348     .499054
         tea |   .0535362   .0611395     0.88   0.385    -.0692975    .1763698
     comform |   -.029029   .1203844    -0.24   0.812    -.2789818    .2209238
         dom |   .0246562   .0372426     0.66   0.510    -.0496861    .0989984
    econfood |  -.0015741   .0142529    -0.11   0.913    -.0308924    .0277442
       house |   .0213515   .0394722     0.54   0.593    -.0596929    .1023959
      llomue |  -.0471791   .0576122    -0.82   0.421    -.1661172     .071759
     chitsua |  -.0965657   .1155646    -0.84   0.407    -.3286638    .1355323
      living |   .0127559   .0123514     1.03   0.309    -.0123192    .0378309
       _cons |  -.1117456   .0720209    -1.55   0.128    -.2572067    .0337155
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.2422
                                                  Largest FMI     =     0.2040
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      82.76
                                                          avg     =      82.76
Within VCE type:       Robust                             max     =      82.76

         diff: _b[civiceduc]-_b[hotline]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |  -.0043781   .0335101    -0.13   0.896    -.0710313    .0622751
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[hotline]

 ( 1)  diff = 0

       F(  1,  82.8) =    0.02
            Prob > F =    0.8964

. scalar define t1_tseen=r(p)

. display t1_tseen
.89636973

. 
. mi estimate (diff: _b[civiceduc]-_b[verdade]), saving(miest, replace): regress tseen civiceduc
>  hotline verdade pr1 pr2 pr3 post post_miss health health_miss sex age single divor protest co
> m prof tea comform dom econfood house llomue chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.8028
                                                  Largest FMI     =     0.6468
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      17.22
                                                          avg     =      38.89
                                                          max     =      92.29
Model F test:       Equal FMI                     F(  25,  133.9) =       3.84
Within VCE type:       Robust                     Prob > F        =     0.0000

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0353357   .0367192     0.96   0.343    -.0394011    .1100724
     hotline |   .0397137   .0355022     1.12   0.269    -.0317391    .1111665
     verdade |   .0671563   .0379074     1.77   0.083    -.0089946    .1433072
         pr1 |   .1623704   .0425134     3.82   0.001     .0752633    .2494775
         pr2 |   .1673535   .0425797     3.93   0.000     .0822704    .2524366
         pr3 |   .2257457   .0432683     5.22   0.000     .1384946    .3129967
        post |  -.0369107   .0403608    -0.91   0.363    -.1170673    .0432459
   post_miss |  -.0501368   .0599875    -0.84   0.415    -.1765778    .0763042
      health |    .046196   .0360693     1.28   0.211    -.0277688    .1201607
 health_miss |   .0778251    .057602     1.35   0.192    -.0424113    .1980616
         sex |   .0541128   .0293608     1.84   0.073    -.0052222    .1134478
         age |   .0032687   .0011145     2.93   0.005     .0010314     .005506
      single |  -.0303396   .0390206    -0.78   0.445    -.1111442     .050465
       divor |  -.1927525   .1341667    -1.44   0.165    -.4711947    .0856898
     protest |   .0266569   .0364904     0.73   0.472    -.0484373    .1017512
         com |  -.0131006   .0658885    -0.20   0.843    -.1450197    .1188185
        prof |   .2686944   .1132802     2.37   0.024     .0383348     .499054
         tea |   .0535362   .0611395     0.88   0.385    -.0692975    .1763698
     comform |   -.029029   .1203844    -0.24   0.812    -.2789818    .2209238
         dom |   .0246562   .0372426     0.66   0.510    -.0496861    .0989984
    econfood |  -.0015741   .0142529    -0.11   0.913    -.0308924    .0277442
       house |   .0213515   .0394722     0.54   0.593    -.0596929    .1023959
      llomue |  -.0471791   .0576122    -0.82   0.421    -.1661172     .071759
     chitsua |  -.0965657   .1155646    -0.84   0.407    -.3286638    .1355323
      living |   .0127559   .0123514     1.03   0.309    -.0123192    .0378309
       _cons |  -.1117456   .0720209    -1.55   0.128    -.2572067    .0337155
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.3212
                                                  Largest FMI     =     0.2556
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      66.98
                                                          avg     =      66.98
Within VCE type:       Robust                             max     =      66.98

         diff: _b[civiceduc]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |  -.0318206   .0355399    -0.90   0.374    -.1027589    .0391176
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[verdade]

 ( 1)  diff = 0

       F(  1,  67.0) =    0.80
            Prob > F =    0.3738

. scalar define t2_tseen=r(p)

. display t2_tseen
.37380649

. 
. mi estimate (diff: _b[hotline]-_b[verdade]), saving(miest, replace): regress tseen civiceduc h
> otline verdade pr1 pr2 pr3 post post_miss health health_miss sex age single divor protest com 
> prof tea comform dom econfood house llomue chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.8028
                                                  Largest FMI     =     0.6468
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      17.22
                                                          avg     =      38.89
                                                          max     =      92.29
Model F test:       Equal FMI                     F(  25,  133.9) =       3.84
Within VCE type:       Robust                     Prob > F        =     0.0000

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0353357   .0367192     0.96   0.343    -.0394011    .1100724
     hotline |   .0397137   .0355022     1.12   0.269    -.0317391    .1111665
     verdade |   .0671563   .0379074     1.77   0.083    -.0089946    .1433072
         pr1 |   .1623704   .0425134     3.82   0.001     .0752633    .2494775
         pr2 |   .1673535   .0425797     3.93   0.000     .0822704    .2524366
         pr3 |   .2257457   .0432683     5.22   0.000     .1384946    .3129967
        post |  -.0369107   .0403608    -0.91   0.363    -.1170673    .0432459
   post_miss |  -.0501368   .0599875    -0.84   0.415    -.1765778    .0763042
      health |    .046196   .0360693     1.28   0.211    -.0277688    .1201607
 health_miss |   .0778251    .057602     1.35   0.192    -.0424113    .1980616
         sex |   .0541128   .0293608     1.84   0.073    -.0052222    .1134478
         age |   .0032687   .0011145     2.93   0.005     .0010314     .005506
      single |  -.0303396   .0390206    -0.78   0.445    -.1111442     .050465
       divor |  -.1927525   .1341667    -1.44   0.165    -.4711947    .0856898
     protest |   .0266569   .0364904     0.73   0.472    -.0484373    .1017512
         com |  -.0131006   .0658885    -0.20   0.843    -.1450197    .1188185
        prof |   .2686944   .1132802     2.37   0.024     .0383348     .499054
         tea |   .0535362   .0611395     0.88   0.385    -.0692975    .1763698
     comform |   -.029029   .1203844    -0.24   0.812    -.2789818    .2209238
         dom |   .0246562   .0372426     0.66   0.510    -.0496861    .0989984
    econfood |  -.0015741   .0142529    -0.11   0.913    -.0308924    .0277442
       house |   .0213515   .0394722     0.54   0.593    -.0596929    .1023959
      llomue |  -.0471791   .0576122    -0.82   0.421    -.1661172     .071759
     chitsua |  -.0965657   .1155646    -0.84   0.407    -.3286638    .1355323
      living |   .0127559   .0123514     1.03   0.309    -.0123192    .0378309
       _cons |  -.1117456   .0720209    -1.55   0.128    -.2572067    .0337155
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.4127
                                                  Largest FMI     =     0.3083
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      54.29
                                                          avg     =      54.29
Within VCE type:       Robust                             max     =      54.29

         diff: _b[hotline]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       tseen |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |  -.0274426   .0396943    -0.69   0.492    -.1070152    .0521301
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[hotline]-_b[verdade]

 ( 1)  diff = 0

       F(  1,  54.3) =    0.48
            Prob > F =    0.4923

. scalar define t3_tseen=r(p)

. display t3_tseen
.49229326

. 
. mi test civiceduc hotline verdade
note: assuming equal fractions of missing information

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,  88.1) =    1.13
            Prob > F =    0.3411

. scalar define t4_tseen=r(p)

. display t4_tseen
.34108763

. 
. mi estimate, dots: regress intt civiceduc hotline verdade pr1 pr2 pr3 post post_miss health he
> alth_miss sex age single divor protest com prof tea comform dom econfood house llomue chitsua 
> living, cluster(ea)

Imputations (10):
  .........10 done

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.7504
                                                  Largest FMI     =     0.7184
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      13.87
                                                          avg     =      53.06
                                                          max     =     142.20
Model F test:       Equal FMI                     F(  25,  135.4) =       3.35
Within VCE type:       Robust                     Prob > F        =     0.0000

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
        intt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0480525   .0203938     2.36   0.020     .0077384    .0883667
     hotline |   .0503416   .0206027     2.44   0.016     .0095745    .0911087
     verdade |   .0346515   .0242108     1.43   0.156    -.0134024    .0827054
         pr1 |   .0589276   .0349406     1.69   0.105    -.0134159    .1312711
         pr2 |   .0884267   .0319205     2.77   0.009      .023663    .1531903
         pr3 |   .0859766    .028108     3.06   0.004     .0295363    .1424168
        post |  -.0324119   .0307765    -1.05   0.298    -.0945458    .0297221
   post_miss |  -.0159091   .0645411    -0.25   0.806    -.1455334    .1137152
      health |   .0093879   .0215216     0.44   0.666     -.034477    .0532528
 health_miss |   .0182497   .0606523     0.30   0.765    -.1046252    .1411247
         sex |   .0465976   .0186654     2.50   0.016     .0091109    .0840843
         age |  -.0001996   .0007896    -0.25   0.802    -.0017981    .0013989
      single |  -.0181826   .0262338    -0.69   0.491    -.0707292    .0343639
       divor |   .0250714   .0951376     0.26   0.794    -.1691057    .2192484
     protest |   .0252847   .0252521     1.00   0.326    -.0267112    .0772806
         com |  -.0966105   .0425582    -2.27   0.026    -.1812968   -.0119242
        prof |   .1892699   .0678454     2.79   0.015     .0436274    .3349123
         tea |   .0531859   .0498165     1.07   0.296    -.0495915    .1559632
     comform |  -.1109119   .0707218    -1.57   0.120    -.2510126    .0291888
         dom |    .001846   .0319153     0.06   0.954    -.0621137    .0658057
    econfood |  -.0148817   .0084464    -1.76   0.084     -.031861    .0020976
       house |   .0200498   .0314904     0.64   0.531    -.0454149    .0855144
      llomue |   -.116209   .0471573    -2.46   0.019    -.2119996   -.0204184
     chitsua |  -.0435357   .0730546    -0.60   0.553    -.1891402    .1020688
      living |   .0358995   .0092057     3.90   0.000     .0174845    .0543145
       _cons |   .5941591   .0565328    10.51   0.000     .4762165    .7121018
------------------------------------------------------------------------------

. estimates store intt

. 
. mi estimate, dots: mean intt if control==1

Imputations (10):
  .........10 done

Multiple-imputation estimates      Imputations     =        10
Mean estimation                    Number of obs   =       452
                                   Average RVI     =    0.1528
                                   Largest FMI     =    0.1365
                                   Complete DF     =       451
DF adjustment:   Small sample      DF:     min     =    221.23
                                           avg     =    221.23
Within VCE type:     Analytic              max     =    221.23

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
        intt |   .7786061   .0174749      .7441676    .8130447
--------------------------------------------------------------

. matrix define aux=e(b_mi)

. scalar define m_intt=aux[1,1]

. display m_intt
.77860614

. 
. mi estimate (diff: _b[civiceduc]-_b[hotline]), saving(miest, replace): regress intt civiceduc 
> hotline verdade pr1 pr2 pr3 post post_miss health health_miss sex age single divor protest com
>  prof tea comform dom econfood house llomue chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.7504
                                                  Largest FMI     =     0.7184
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      13.87
                                                          avg     =      53.06
                                                          max     =     142.20
Model F test:       Equal FMI                     F(  25,  135.4) =       3.35
Within VCE type:       Robust                     Prob > F        =     0.0000

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
        intt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0480525   .0203938     2.36   0.020     .0077384    .0883667
     hotline |   .0503416   .0206027     2.44   0.016     .0095745    .0911087
     verdade |   .0346515   .0242108     1.43   0.156    -.0134024    .0827054
         pr1 |   .0589276   .0349406     1.69   0.105    -.0134159    .1312711
         pr2 |   .0884267   .0319205     2.77   0.009      .023663    .1531903
         pr3 |   .0859766    .028108     3.06   0.004     .0295363    .1424168
        post |  -.0324119   .0307765    -1.05   0.298    -.0945458    .0297221
   post_miss |  -.0159091   .0645411    -0.25   0.806    -.1455334    .1137152
      health |   .0093879   .0215216     0.44   0.666     -.034477    .0532528
 health_miss |   .0182497   .0606523     0.30   0.765    -.1046252    .1411247
         sex |   .0465976   .0186654     2.50   0.016     .0091109    .0840843
         age |  -.0001996   .0007896    -0.25   0.802    -.0017981    .0013989
      single |  -.0181826   .0262338    -0.69   0.491    -.0707292    .0343639
       divor |   .0250714   .0951376     0.26   0.794    -.1691057    .2192484
     protest |   .0252847   .0252521     1.00   0.326    -.0267112    .0772806
         com |  -.0966105   .0425582    -2.27   0.026    -.1812968   -.0119242
        prof |   .1892699   .0678454     2.79   0.015     .0436274    .3349123
         tea |   .0531859   .0498165     1.07   0.296    -.0495915    .1559632
     comform |  -.1109119   .0707218    -1.57   0.120    -.2510126    .0291888
         dom |    .001846   .0319153     0.06   0.954    -.0621137    .0658057
    econfood |  -.0148817   .0084464    -1.76   0.084     -.031861    .0020976
       house |   .0200498   .0314904     0.64   0.531    -.0454149    .0855144
      llomue |   -.116209   .0471573    -2.46   0.019    -.2119996   -.0204184
     chitsua |  -.0435357   .0730546    -0.60   0.553    -.1891402    .1020688
      living |   .0358995   .0092057     3.90   0.000     .0174845    .0543145
       _cons |   .5941591   .0565328    10.51   0.000     .4762165    .7121018
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.1998
                                                  Largest FMI     =     0.1737
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      93.68
                                                          avg     =      93.68
Within VCE type:       Robust                             max     =      93.68

         diff: _b[civiceduc]-_b[hotline]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
        intt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |   -.002289   .0203954    -0.11   0.911    -.0427863    .0382082
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[hotline]

 ( 1)  diff = 0

       F(  1,  93.7) =    0.01
            Prob > F =    0.9109

. scalar define t1_intt=r(p)

. display t1_intt
.91087891

. 
. mi estimate (diff: _b[civiceduc]-_b[verdade]), saving(miest, replace): regress intt civiceduc 
> hotline verdade pr1 pr2 pr3 post post_miss health health_miss sex age single divor protest com
>  prof tea comform dom econfood house llomue chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.7504
                                                  Largest FMI     =     0.7184
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      13.87
                                                          avg     =      53.06
                                                          max     =     142.20
Model F test:       Equal FMI                     F(  25,  135.4) =       3.35
Within VCE type:       Robust                     Prob > F        =     0.0000

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
        intt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0480525   .0203938     2.36   0.020     .0077384    .0883667
     hotline |   .0503416   .0206027     2.44   0.016     .0095745    .0911087
     verdade |   .0346515   .0242108     1.43   0.156    -.0134024    .0827054
         pr1 |   .0589276   .0349406     1.69   0.105    -.0134159    .1312711
         pr2 |   .0884267   .0319205     2.77   0.009      .023663    .1531903
         pr3 |   .0859766    .028108     3.06   0.004     .0295363    .1424168
        post |  -.0324119   .0307765    -1.05   0.298    -.0945458    .0297221
   post_miss |  -.0159091   .0645411    -0.25   0.806    -.1455334    .1137152
      health |   .0093879   .0215216     0.44   0.666     -.034477    .0532528
 health_miss |   .0182497   .0606523     0.30   0.765    -.1046252    .1411247
         sex |   .0465976   .0186654     2.50   0.016     .0091109    .0840843
         age |  -.0001996   .0007896    -0.25   0.802    -.0017981    .0013989
      single |  -.0181826   .0262338    -0.69   0.491    -.0707292    .0343639
       divor |   .0250714   .0951376     0.26   0.794    -.1691057    .2192484
     protest |   .0252847   .0252521     1.00   0.326    -.0267112    .0772806
         com |  -.0966105   .0425582    -2.27   0.026    -.1812968   -.0119242
        prof |   .1892699   .0678454     2.79   0.015     .0436274    .3349123
         tea |   .0531859   .0498165     1.07   0.296    -.0495915    .1559632
     comform |  -.1109119   .0707218    -1.57   0.120    -.2510126    .0291888
         dom |    .001846   .0319153     0.06   0.954    -.0621137    .0658057
    econfood |  -.0148817   .0084464    -1.76   0.084     -.031861    .0020976
       house |   .0200498   .0314904     0.64   0.531    -.0454149    .0855144
      llomue |   -.116209   .0471573    -2.46   0.019    -.2119996   -.0204184
     chitsua |  -.0435357   .0730546    -0.60   0.553    -.1891402    .1020688
      living |   .0358995   .0092057     3.90   0.000     .0174845    .0543145
       _cons |   .5941591   .0565328    10.51   0.000     .4762165    .7121018
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.2520
                                                  Largest FMI     =     0.2107
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      80.49
                                                          avg     =      80.49
Within VCE type:       Robust                             max     =      80.49

         diff: _b[civiceduc]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
        intt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |    .013401   .0247871     0.54   0.590    -.0359222    .0627242
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[verdade]

 ( 1)  diff = 0

       F(  1,  80.5) =    0.29
            Prob > F =    0.5902

. scalar define t2_intt=r(p)

. display t2_intt
.59024514

. 
. mi estimate (diff: _b[hotline]-_b[verdade]), saving(miest, replace): regress intt civiceduc ho
> tline verdade pr1 pr2 pr3 post post_miss health health_miss sex age single divor protest com p
> rof tea comform dom econfood house llomue chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.7504
                                                  Largest FMI     =     0.7184
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      13.87
                                                          avg     =      53.06
                                                          max     =     142.20
Model F test:       Equal FMI                     F(  25,  135.4) =       3.35
Within VCE type:       Robust                     Prob > F        =     0.0000

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
        intt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0480525   .0203938     2.36   0.020     .0077384    .0883667
     hotline |   .0503416   .0206027     2.44   0.016     .0095745    .0911087
     verdade |   .0346515   .0242108     1.43   0.156    -.0134024    .0827054
         pr1 |   .0589276   .0349406     1.69   0.105    -.0134159    .1312711
         pr2 |   .0884267   .0319205     2.77   0.009      .023663    .1531903
         pr3 |   .0859766    .028108     3.06   0.004     .0295363    .1424168
        post |  -.0324119   .0307765    -1.05   0.298    -.0945458    .0297221
   post_miss |  -.0159091   .0645411    -0.25   0.806    -.1455334    .1137152
      health |   .0093879   .0215216     0.44   0.666     -.034477    .0532528
 health_miss |   .0182497   .0606523     0.30   0.765    -.1046252    .1411247
         sex |   .0465976   .0186654     2.50   0.016     .0091109    .0840843
         age |  -.0001996   .0007896    -0.25   0.802    -.0017981    .0013989
      single |  -.0181826   .0262338    -0.69   0.491    -.0707292    .0343639
       divor |   .0250714   .0951376     0.26   0.794    -.1691057    .2192484
     protest |   .0252847   .0252521     1.00   0.326    -.0267112    .0772806
         com |  -.0966105   .0425582    -2.27   0.026    -.1812968   -.0119242
        prof |   .1892699   .0678454     2.79   0.015     .0436274    .3349123
         tea |   .0531859   .0498165     1.07   0.296    -.0495915    .1559632
     comform |  -.1109119   .0707218    -1.57   0.120    -.2510126    .0291888
         dom |    .001846   .0319153     0.06   0.954    -.0621137    .0658057
    econfood |  -.0148817   .0084464    -1.76   0.084     -.031861    .0020976
       house |   .0200498   .0314904     0.64   0.531    -.0454149    .0855144
      llomue |   -.116209   .0471573    -2.46   0.019    -.2119996   -.0204184
     chitsua |  -.0435357   .0730546    -0.60   0.553    -.1891402    .1020688
      living |   .0358995   .0092057     3.90   0.000     .0174845    .0543145
       _cons |   .5941591   .0565328    10.51   0.000     .4762165    .7121018
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.2715
                                                  Largest FMI     =     0.2239
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      76.26
                                                          avg     =      76.26
Within VCE type:       Robust                             max     =      76.26

         diff: _b[hotline]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
        intt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |     .01569   .0239127     0.66   0.514    -.0319336    .0633137
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[hotline]-_b[verdade]

 ( 1)  diff = 0

       F(  1,  76.3) =    0.43
            Prob > F =    0.5137

. scalar define t3_intt=r(p)

. display t3_intt
.51371037

. 
. mi test civiceduc hotline verdade
note: assuming equal fractions of missing information

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3, 133.2) =    2.27
            Prob > F =    0.0830

. scalar define t4_intt=r(p)

. display t4_intt
.08300415

. 
. mi estimate, dots: regress carta civiceduc hotline verdade pr1 pr2 pr3 market market_miss sex 
> age divor school protest relig com tea econfood econmedic chitsua, cluster(ea)

Imputations (10):
  .........10 done

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.5690
                                                  Largest FMI     =     0.5878
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      20.63
                                                          avg     =      60.78
                                                          max     =     125.98
Model F test:       Equal FMI                     F(  19,  137.0) =       2.61
Within VCE type:       Robust                     Prob > F        =     0.0007

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       carta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0377597   .0372892     1.01   0.315    -.0366311    .1121505
     hotline |  -.0141125    .029275    -0.48   0.632    -.0726879     .044463
     verdade |    .062694   .0375691     1.67   0.098    -.0118387    .1372268
         pr1 |   .0127499   .0445332     0.29   0.775    -.0760157    .1015155
         pr2 |  -.0152551   .0358272    -0.43   0.671    -.0861562    .0556459
         pr3 |   .0746909   .0397613     1.88   0.063    -.0040973    .1534792
      market |   .0077602   .0313998     0.25   0.806    -.0552327    .0707532
 market_miss |  -.0751889   .0499382    -1.51   0.140     -.175924    .0255463
         sex |   .0027478   .0233296     0.12   0.907    -.0442533    .0497488
         age |  -.0014811    .000792    -1.87   0.065    -.0030595    .0000974
       divor |   .0290205   .1420523     0.20   0.840    -.2667182    .3247593
      school |   .0125901   .0081173     1.55   0.124    -.0035195    .0286998
     protest |  -.0012208   .0311274    -0.04   0.969    -.0644375    .0619958
       relig |     .02533   .0115079     2.20   0.036     .0017957    .0488643
         com |  -.0361489   .0511333    -0.71   0.484    -.1393592    .0670615
         tea |   .1818747    .071304     2.55   0.016     .0360226    .3277268
    econfood |  -.0036098   .0102965    -0.35   0.727    -.0241015    .0168819
   econmedic |  -.0060205   .0105558    -0.57   0.571    -.0271419     .015101
     chitsua |     .04659    .102038     0.46   0.650     -.158751    .2519309
       _cons |   .0723284   .0770231     0.94   0.356     -.085698    .2303548
------------------------------------------------------------------------------

. estimates store carta

. 
. mi estimate, dots: mean carta if control==1

Imputations (10):
  .........10 done

Multiple-imputation estimates      Imputations     =        10
Mean estimation                    Number of obs   =       452
                                   Average RVI     =    0.5409
                                   Largest FMI     =    0.3694
                                   Complete DF     =       451
DF adjustment:   Small sample      DF:     min     =     58.40
                                           avg     =     58.40
Within VCE type:     Analytic              max     =     58.40

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
       carta |   .1632743   .0215939      .1200557    .2064929
--------------------------------------------------------------

. matrix define aux=e(b_mi)

. scalar define m_carta=aux[1,1]

. display m_carta
.16327434

. 
. mi estimate (diff: _b[civiceduc]-_b[hotline]), saving(miest, replace): regress carta civiceduc
>  hotline verdade pr1 pr2 pr3 market market_miss sex age divor school protest relig com tea eco
> nfood econmedic chitsua, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.5690
                                                  Largest FMI     =     0.5878
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      20.63
                                                          avg     =      60.78
                                                          max     =     125.98
Model F test:       Equal FMI                     F(  19,  137.0) =       2.61
Within VCE type:       Robust                     Prob > F        =     0.0007

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       carta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0377597   .0372892     1.01   0.315    -.0366311    .1121505
     hotline |  -.0141125    .029275    -0.48   0.632    -.0726879     .044463
     verdade |    .062694   .0375691     1.67   0.098    -.0118387    .1372268
         pr1 |   .0127499   .0445332     0.29   0.775    -.0760157    .1015155
         pr2 |  -.0152551   .0358272    -0.43   0.671    -.0861562    .0556459
         pr3 |   .0746909   .0397613     1.88   0.063    -.0040973    .1534792
      market |   .0077602   .0313998     0.25   0.806    -.0552327    .0707532
 market_miss |  -.0751889   .0499382    -1.51   0.140     -.175924    .0255463
         sex |   .0027478   .0233296     0.12   0.907    -.0442533    .0497488
         age |  -.0014811    .000792    -1.87   0.065    -.0030595    .0000974
       divor |   .0290205   .1420523     0.20   0.840    -.2667182    .3247593
      school |   .0125901   .0081173     1.55   0.124    -.0035195    .0286998
     protest |  -.0012208   .0311274    -0.04   0.969    -.0644375    .0619958
       relig |     .02533   .0115079     2.20   0.036     .0017957    .0488643
         com |  -.0361489   .0511333    -0.71   0.484    -.1393592    .0670615
         tea |   .1818747    .071304     2.55   0.016     .0360226    .3277268
    econfood |  -.0036098   .0102965    -0.35   0.727    -.0241015    .0168819
   econmedic |  -.0060205   .0105558    -0.57   0.571    -.0271419     .015101
     chitsua |     .04659    .102038     0.46   0.650     -.158751    .2519309
       _cons |   .0723284   .0770231     0.94   0.356     -.085698    .2303548
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.1976
                                                  Largest FMI     =     0.1720
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      94.32
                                                          avg     =      94.32
Within VCE type:       Robust                             max     =      94.32

         diff: _b[civiceduc]-_b[hotline]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       carta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |   .0518722   .0340709     1.52   0.131    -.0157734    .1195177
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[hotline]

 ( 1)  diff = 0

       F(  1,  94.3) =    2.32
            Prob > F =    0.1312

. scalar define t1_carta=r(p)

. display t1_carta
.13123501

. 
. mi estimate (diff: _b[civiceduc]-_b[verdade]), saving(miest, replace): regress carta civiceduc
>  hotline verdade pr1 pr2 pr3 market market_miss sex age divor school protest relig com tea eco
> nfood econmedic chitsua, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.5690
                                                  Largest FMI     =     0.5878
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      20.63
                                                          avg     =      60.78
                                                          max     =     125.98
Model F test:       Equal FMI                     F(  19,  137.0) =       2.61
Within VCE type:       Robust                     Prob > F        =     0.0007

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       carta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0377597   .0372892     1.01   0.315    -.0366311    .1121505
     hotline |  -.0141125    .029275    -0.48   0.632    -.0726879     .044463
     verdade |    .062694   .0375691     1.67   0.098    -.0118387    .1372268
         pr1 |   .0127499   .0445332     0.29   0.775    -.0760157    .1015155
         pr2 |  -.0152551   .0358272    -0.43   0.671    -.0861562    .0556459
         pr3 |   .0746909   .0397613     1.88   0.063    -.0040973    .1534792
      market |   .0077602   .0313998     0.25   0.806    -.0552327    .0707532
 market_miss |  -.0751889   .0499382    -1.51   0.140     -.175924    .0255463
         sex |   .0027478   .0233296     0.12   0.907    -.0442533    .0497488
         age |  -.0014811    .000792    -1.87   0.065    -.0030595    .0000974
       divor |   .0290205   .1420523     0.20   0.840    -.2667182    .3247593
      school |   .0125901   .0081173     1.55   0.124    -.0035195    .0286998
     protest |  -.0012208   .0311274    -0.04   0.969    -.0644375    .0619958
       relig |     .02533   .0115079     2.20   0.036     .0017957    .0488643
         com |  -.0361489   .0511333    -0.71   0.484    -.1393592    .0670615
         tea |   .1818747    .071304     2.55   0.016     .0360226    .3277268
    econfood |  -.0036098   .0102965    -0.35   0.727    -.0241015    .0168819
   econmedic |  -.0060205   .0105558    -0.57   0.571    -.0271419     .015101
     chitsua |     .04659    .102038     0.46   0.650     -.158751    .2519309
       _cons |   .0723284   .0770231     0.94   0.356     -.085698    .2303548
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.1376
                                                  Largest FMI     =     0.1253
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =     113.34
                                                          avg     =     113.34
Within VCE type:       Robust                             max     =     113.34

         diff: _b[civiceduc]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       carta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |  -.0249343   .0425532    -0.59   0.559    -.1092371    .0593685
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[verdade]

 ( 1)  diff = 0

       F(  1, 113.3) =    0.34
            Prob > F =    0.5591

. scalar define t2_carta=r(p)

. display t2_carta
.55906951

. 
. mi estimate (diff: _b[hotline]-_b[verdade]), saving(miest, replace): regress carta civiceduc h
> otline verdade pr1 pr2 pr3 market market_miss sex age divor school protest relig com tea econf
> ood econmedic chitsua, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.5690
                                                  Largest FMI     =     0.5878
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      20.63
                                                          avg     =      60.78
                                                          max     =     125.98
Model F test:       Equal FMI                     F(  19,  137.0) =       2.61
Within VCE type:       Robust                     Prob > F        =     0.0007

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       carta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0377597   .0372892     1.01   0.315    -.0366311    .1121505
     hotline |  -.0141125    .029275    -0.48   0.632    -.0726879     .044463
     verdade |    .062694   .0375691     1.67   0.098    -.0118387    .1372268
         pr1 |   .0127499   .0445332     0.29   0.775    -.0760157    .1015155
         pr2 |  -.0152551   .0358272    -0.43   0.671    -.0861562    .0556459
         pr3 |   .0746909   .0397613     1.88   0.063    -.0040973    .1534792
      market |   .0077602   .0313998     0.25   0.806    -.0552327    .0707532
 market_miss |  -.0751889   .0499382    -1.51   0.140     -.175924    .0255463
         sex |   .0027478   .0233296     0.12   0.907    -.0442533    .0497488
         age |  -.0014811    .000792    -1.87   0.065    -.0030595    .0000974
       divor |   .0290205   .1420523     0.20   0.840    -.2667182    .3247593
      school |   .0125901   .0081173     1.55   0.124    -.0035195    .0286998
     protest |  -.0012208   .0311274    -0.04   0.969    -.0644375    .0619958
       relig |     .02533   .0115079     2.20   0.036     .0017957    .0488643
         com |  -.0361489   .0511333    -0.71   0.484    -.1393592    .0670615
         tea |   .1818747    .071304     2.55   0.016     .0360226    .3277268
    econfood |  -.0036098   .0102965    -0.35   0.727    -.0241015    .0168819
   econmedic |  -.0060205   .0105558    -0.57   0.571    -.0271419     .015101
     chitsua |     .04659    .102038     0.46   0.650     -.158751    .2519309
       _cons |   .0723284   .0770231     0.94   0.356     -.085698    .2303548
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.3175
                                                  Largest FMI     =     0.2533
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      67.62
                                                          avg     =      67.62
Within VCE type:       Robust                             max     =      67.62

         diff: _b[hotline]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
       carta |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |  -.0768065   .0372888    -2.06   0.043    -.1512227   -.0023903
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[hotline]-_b[verdade]

 ( 1)  diff = 0

       F(  1,  67.6) =    4.24
            Prob > F =    0.0433

. scalar define t3_carta=r(p)

. display t3_carta
.04327242

. 
. mi test civiceduc hotline verdade
note: assuming equal fractions of missing information

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3, 114.2) =    1.90
            Prob > F =    0.1331

. scalar define t4_carta=r(p)

. display t4_carta
.13308452

. 
. mi estimate, dots: regress guebas2 civiceduc hotline verdade pr1 pr2 pr3 post post_miss health
>  health_miss police police_miss sex age single divor norelig protest com prof comform econfood
>  house oven lchang llomue lchuabo lchitewe lronga chitsua living, cluster(ea)

Imputations (10):
  .........10 done

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.9971
                                                  Largest FMI     =     0.7202
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      13.80
                                                          avg     =      35.81
                                                          max     =      77.32
Model F test:       Equal FMI                     F(  31,  133.1) =       2.02
Within VCE type:       Robust                     Prob > F        =     0.0033

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0403326   .0311578     1.29   0.203    -.0225643    .1032295
     hotline |   .0340997    .026519     1.29   0.202    -.0187029    .0869023
     verdade |   .0111677   .0324608     0.34   0.732    -.0540082    .0763435
         pr1 |  -.1830158   .0597219    -3.06   0.004    -.3038163   -.0622154
         pr2 |  -.0941386   .0661646    -1.42   0.165    -.2292413     .040964
         pr3 |  -.0512816   .0648973    -0.79   0.433    -.1817237    .0791605
        post |   .0065239   .0571253     0.11   0.910    -.1132928    .1263406
   post_miss |   .0377623   .0625057     0.60   0.549    -.0879913     .163516
      health |   .0383109   .0268133     1.43   0.164    -.0164871    .0931089
 health_miss |   .0981538   .0755503     1.30   0.211    -.0611753    .2574829
      police |  -.0470511   .0396745    -1.19   0.245     -.128117    .0340148
 police_miss |  -.2340634   .1566138    -1.49   0.154    -.5657782    .0976515
         sex |    .007018   .0260259     0.27   0.790    -.0474429    .0614789
         age |  -.0013668   .0009766    -1.40   0.169    -.0033383    .0006048
      single |  -.0487332   .0367222    -1.33   0.199    -.1251321    .0276657
       divor |  -.1005908   .1517931    -0.66   0.514    -.4135888    .2124073
     norelig |  -.0743981   .0673559    -1.10   0.279    -.2126144    .0638183
     protest |  -.0041248   .0309559    -0.13   0.895    -.0674141    .0591645
         com |  -.0187778   .0529061    -0.35   0.724    -.1256198    .0880641
        prof |   .0195978   .1065268     0.18   0.857    -.2091981    .2483936
     comform |  -.1603161   .1420734    -1.13   0.277    -.4630792     .142447
    econfood |   -.010864    .011578    -0.94   0.359    -.0349138    .0131859
       house |   .0527851   .0303368     1.74   0.087    -.0078037    .1133738
        oven |   .0609527   .0386528     1.58   0.121    -.0166066     .138512
      lchang |   .0231415   .0552319     0.42   0.677    -.0877799     .134063
      llomue |   .0725496   .0588929     1.23   0.226    -.0468175    .1919166
     lchuabo |   .0019593   .0592978     0.03   0.974    -.1185239    .1224425
    lchitewe |  -.1110985   .1329124    -0.84   0.406    -.3768553    .1546583
      lronga |  -.0465038    .041766    -1.11   0.272    -.1309549    .0379473
     chitsua |   .0720059   .1150258     0.63   0.542    -.1750302     .319042
      living |   .0389406   .0108617     3.59   0.001     .0172046    .0606765
       _cons |   .7722555   .0588879    13.11   0.000     .6528596    .8916515
------------------------------------------------------------------------------

. estimates store guebas2

. 
. mi estimate, dots: mean guebas2 if control==1

Imputations (10):
  .........10 done

Multiple-imputation estimates      Imputations     =        10
Mean estimation                    Number of obs   =       452
                                   Average RVI     =    0.3781
                                   Largest FMI     =    0.2873
                                   Complete DF     =       451
DF adjustment:   Small sample      DF:     min     =     87.46
                                           avg     =     87.46
Within VCE type:     Analytic              max     =     87.46

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
     guebas2 |   .8256637   .0209655      .7839957    .8673318
--------------------------------------------------------------

. matrix define aux=e(b_mi)

. scalar define m_guebas2=aux[1,1]

. display m_guebas2
.82566372

. 
. mi estimate (diff: _b[civiceduc]-_b[hotline]), saving(miest, replace): regress guebas2 civiced
> uc hotline verdade pr1 pr2 pr3 post post_miss health health_miss police police_miss sex age si
> ngle divor norelig protest com prof comform econfood house oven lchang llomue lchuabo lchitewe
>  lronga chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.9971
                                                  Largest FMI     =     0.7202
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      13.80
                                                          avg     =      35.81
                                                          max     =      77.32
Model F test:       Equal FMI                     F(  31,  133.1) =       2.02
Within VCE type:       Robust                     Prob > F        =     0.0033

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0403326   .0311578     1.29   0.203    -.0225643    .1032295
     hotline |   .0340997    .026519     1.29   0.202    -.0187029    .0869023
     verdade |   .0111677   .0324608     0.34   0.732    -.0540082    .0763435
         pr1 |  -.1830158   .0597219    -3.06   0.004    -.3038163   -.0622154
         pr2 |  -.0941386   .0661646    -1.42   0.165    -.2292413     .040964
         pr3 |  -.0512816   .0648973    -0.79   0.433    -.1817237    .0791605
        post |   .0065239   .0571253     0.11   0.910    -.1132928    .1263406
   post_miss |   .0377623   .0625057     0.60   0.549    -.0879913     .163516
      health |   .0383109   .0268133     1.43   0.164    -.0164871    .0931089
 health_miss |   .0981538   .0755503     1.30   0.211    -.0611753    .2574829
      police |  -.0470511   .0396745    -1.19   0.245     -.128117    .0340148
 police_miss |  -.2340634   .1566138    -1.49   0.154    -.5657782    .0976515
         sex |    .007018   .0260259     0.27   0.790    -.0474429    .0614789
         age |  -.0013668   .0009766    -1.40   0.169    -.0033383    .0006048
      single |  -.0487332   .0367222    -1.33   0.199    -.1251321    .0276657
       divor |  -.1005908   .1517931    -0.66   0.514    -.4135888    .2124073
     norelig |  -.0743981   .0673559    -1.10   0.279    -.2126144    .0638183
     protest |  -.0041248   .0309559    -0.13   0.895    -.0674141    .0591645
         com |  -.0187778   .0529061    -0.35   0.724    -.1256198    .0880641
        prof |   .0195978   .1065268     0.18   0.857    -.2091981    .2483936
     comform |  -.1603161   .1420734    -1.13   0.277    -.4630792     .142447
    econfood |   -.010864    .011578    -0.94   0.359    -.0349138    .0131859
       house |   .0527851   .0303368     1.74   0.087    -.0078037    .1133738
        oven |   .0609527   .0386528     1.58   0.121    -.0166066     .138512
      lchang |   .0231415   .0552319     0.42   0.677    -.0877799     .134063
      llomue |   .0725496   .0588929     1.23   0.226    -.0468175    .1919166
     lchuabo |   .0019593   .0592978     0.03   0.974    -.1185239    .1224425
    lchitewe |  -.1110985   .1329124    -0.84   0.406    -.3768553    .1546583
      lronga |  -.0465038    .041766    -1.11   0.272    -.1309549    .0379473
     chitsua |   .0720059   .1150258     0.63   0.542    -.1750302     .319042
      living |   .0389406   .0108617     3.59   0.001     .0172046    .0606765
       _cons |   .7722555   .0588879    13.11   0.000     .6528596    .8916515
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.8562
                                                  Largest FMI     =     0.4895
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      28.26
                                                          avg     =      28.26
Within VCE type:       Robust                             max     =      28.26

         diff: _b[civiceduc]-_b[hotline]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |   .0062329   .0317601     0.20   0.846    -.0587979    .0712637
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[hotline]

 ( 1)  diff = 0

       F(  1,  28.3) =    0.04
            Prob > F =    0.8458

. scalar define t1_guebas2=r(p)

. display t1_guebas2
.84581914

. 
. mi estimate (diff: _b[civiceduc]-_b[verdade]), saving(miest, replace): regress guebas2 civiced
> uc hotline verdade pr1 pr2 pr3 post post_miss health health_miss police police_miss sex age si
> ngle divor norelig protest com prof comform econfood house oven lchang llomue lchuabo lchitewe
>  lronga chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.9971
                                                  Largest FMI     =     0.7202
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      13.80
                                                          avg     =      35.81
                                                          max     =      77.32
Model F test:       Equal FMI                     F(  31,  133.1) =       2.02
Within VCE type:       Robust                     Prob > F        =     0.0033

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0403326   .0311578     1.29   0.203    -.0225643    .1032295
     hotline |   .0340997    .026519     1.29   0.202    -.0187029    .0869023
     verdade |   .0111677   .0324608     0.34   0.732    -.0540082    .0763435
         pr1 |  -.1830158   .0597219    -3.06   0.004    -.3038163   -.0622154
         pr2 |  -.0941386   .0661646    -1.42   0.165    -.2292413     .040964
         pr3 |  -.0512816   .0648973    -0.79   0.433    -.1817237    .0791605
        post |   .0065239   .0571253     0.11   0.910    -.1132928    .1263406
   post_miss |   .0377623   .0625057     0.60   0.549    -.0879913     .163516
      health |   .0383109   .0268133     1.43   0.164    -.0164871    .0931089
 health_miss |   .0981538   .0755503     1.30   0.211    -.0611753    .2574829
      police |  -.0470511   .0396745    -1.19   0.245     -.128117    .0340148
 police_miss |  -.2340634   .1566138    -1.49   0.154    -.5657782    .0976515
         sex |    .007018   .0260259     0.27   0.790    -.0474429    .0614789
         age |  -.0013668   .0009766    -1.40   0.169    -.0033383    .0006048
      single |  -.0487332   .0367222    -1.33   0.199    -.1251321    .0276657
       divor |  -.1005908   .1517931    -0.66   0.514    -.4135888    .2124073
     norelig |  -.0743981   .0673559    -1.10   0.279    -.2126144    .0638183
     protest |  -.0041248   .0309559    -0.13   0.895    -.0674141    .0591645
         com |  -.0187778   .0529061    -0.35   0.724    -.1256198    .0880641
        prof |   .0195978   .1065268     0.18   0.857    -.2091981    .2483936
     comform |  -.1603161   .1420734    -1.13   0.277    -.4630792     .142447
    econfood |   -.010864    .011578    -0.94   0.359    -.0349138    .0131859
       house |   .0527851   .0303368     1.74   0.087    -.0078037    .1133738
        oven |   .0609527   .0386528     1.58   0.121    -.0166066     .138512
      lchang |   .0231415   .0552319     0.42   0.677    -.0877799     .134063
      llomue |   .0725496   .0588929     1.23   0.226    -.0468175    .1919166
     lchuabo |   .0019593   .0592978     0.03   0.974    -.1185239    .1224425
    lchitewe |  -.1110985   .1329124    -0.84   0.406    -.3768553    .1546583
      lronga |  -.0465038    .041766    -1.11   0.272    -.1309549    .0379473
     chitsua |   .0720059   .1150258     0.63   0.542    -.1750302     .319042
      living |   .0389406   .0108617     3.59   0.001     .0172046    .0606765
       _cons |   .7722555   .0588879    13.11   0.000     .6528596    .8916515
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.4372
                                                  Largest FMI     =     0.3214
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      51.61
                                                          avg     =      51.61
Within VCE type:       Robust                             max     =      51.61

         diff: _b[civiceduc]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |   .0291649   .0317042     0.92   0.362    -.0344656    .0927955
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[verdade]

 ( 1)  diff = 0

       F(  1,  51.6) =    0.85
            Prob > F =    0.3619

. scalar define t2_guebas2=r(p)

. display t2_guebas2
.36190029

. 
. mi estimate (diff: _b[hotline]-_b[verdade]), saving(miest, replace): regress guebas2 civiceduc
>  hotline verdade pr1 pr2 pr3 post post_miss health health_miss police police_miss sex age sing
> le divor norelig protest com prof comform econfood house oven lchang llomue lchuabo lchitewe l
> ronga chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     0.9971
                                                  Largest FMI     =     0.7202
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      13.80
                                                          avg     =      35.81
                                                          max     =      77.32
Model F test:       Equal FMI                     F(  31,  133.1) =       2.02
Within VCE type:       Robust                     Prob > F        =     0.0033

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0403326   .0311578     1.29   0.203    -.0225643    .1032295
     hotline |   .0340997    .026519     1.29   0.202    -.0187029    .0869023
     verdade |   .0111677   .0324608     0.34   0.732    -.0540082    .0763435
         pr1 |  -.1830158   .0597219    -3.06   0.004    -.3038163   -.0622154
         pr2 |  -.0941386   .0661646    -1.42   0.165    -.2292413     .040964
         pr3 |  -.0512816   .0648973    -0.79   0.433    -.1817237    .0791605
        post |   .0065239   .0571253     0.11   0.910    -.1132928    .1263406
   post_miss |   .0377623   .0625057     0.60   0.549    -.0879913     .163516
      health |   .0383109   .0268133     1.43   0.164    -.0164871    .0931089
 health_miss |   .0981538   .0755503     1.30   0.211    -.0611753    .2574829
      police |  -.0470511   .0396745    -1.19   0.245     -.128117    .0340148
 police_miss |  -.2340634   .1566138    -1.49   0.154    -.5657782    .0976515
         sex |    .007018   .0260259     0.27   0.790    -.0474429    .0614789
         age |  -.0013668   .0009766    -1.40   0.169    -.0033383    .0006048
      single |  -.0487332   .0367222    -1.33   0.199    -.1251321    .0276657
       divor |  -.1005908   .1517931    -0.66   0.514    -.4135888    .2124073
     norelig |  -.0743981   .0673559    -1.10   0.279    -.2126144    .0638183
     protest |  -.0041248   .0309559    -0.13   0.895    -.0674141    .0591645
         com |  -.0187778   .0529061    -0.35   0.724    -.1256198    .0880641
        prof |   .0195978   .1065268     0.18   0.857    -.2091981    .2483936
     comform |  -.1603161   .1420734    -1.13   0.277    -.4630792     .142447
    econfood |   -.010864    .011578    -0.94   0.359    -.0349138    .0131859
       house |   .0527851   .0303368     1.74   0.087    -.0078037    .1133738
        oven |   .0609527   .0386528     1.58   0.121    -.0166066     .138512
      lchang |   .0231415   .0552319     0.42   0.677    -.0877799     .134063
      llomue |   .0725496   .0588929     1.23   0.226    -.0468175    .1919166
     lchuabo |   .0019593   .0592978     0.03   0.974    -.1185239    .1224425
    lchitewe |  -.1110985   .1329124    -0.84   0.406    -.3768553    .1546583
      lronga |  -.0465038    .041766    -1.11   0.272    -.1309549    .0379473
     chitsua |   .0720059   .1150258     0.63   0.542    -.1750302     .319042
      living |   .0389406   .0108617     3.59   0.001     .0172046    .0606765
       _cons |   .7722555   .0588879    13.11   0.000     .6528596    .8916515
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.7833
                                                  Largest FMI     =     0.4661
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      30.56
                                                          avg     =      30.56
Within VCE type:       Robust                             max     =      30.56

         diff: _b[hotline]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     guebas2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |    .022932   .0337333     0.68   0.502    -.0459074    .0917715
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[hotline]-_b[verdade]

 ( 1)  diff = 0

       F(  1,  30.6) =    0.46
            Prob > F =    0.5017

. scalar define t3_guebas2=r(p)

. display t3_guebas2
.50174571

. 
. mi test civiceduc hotline verdade
note: assuming equal fractions of missing information

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,  76.6) =    0.76
            Prob > F =    0.5218

. scalar define t4_guebas2=r(p)

. display t4_guebas2
.52181193

. 
. mi estimate, dots: regress dlakhama2 civiceduc hotline verdade pr1 pr2 pr3 post post_miss heal
> th health_miss police police_miss sex age single divor norelig protest com prof comform econfo
> od house oven lchang llomue lchuabo lchitewe lronga chitsua living, cluster(ea)

Imputations (10):
  .........10 done

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     2.4282
                                                  Largest FMI     =     0.9502
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =       5.15
                                                          avg     =      20.09
                                                          max     =      63.42
Model F test:       Equal FMI                     F(  31,  109.7) =       0.38
Within VCE type:       Robust                     Prob > F        =     0.9986

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0128199   .0133199    -0.96   0.341    -.0397102    .0140704
     hotline |  -.0026767   .0168373    -0.16   0.875    -.0377107    .0323574
     verdade |  -.0023663   .0200242    -0.12   0.907    -.0446607     .039928
         pr1 |  -.0109128   .0205625    -0.53   0.597    -.0519983    .0301728
         pr2 |   .0351322   .0612314     0.57   0.580    -.1026015    .1728658
         pr3 |   .0113837   .0621188     0.18   0.859    -.1289013    .1516688
        post |  -.0199909   .0318761    -0.63   0.544    -.0906101    .0506283
   post_miss |   .0122905   .0498232     0.25   0.809    -.0963691      .12095
      health |    .005785   .0155293     0.37   0.714    -.0269694    .0385394
 health_miss |  -.0339183   .0483804    -0.70   0.500    -.1425532    .0747167
      police |  -.0072577   .0243232    -0.30   0.771    -.0606528    .0461374
 police_miss |   .0661271   .0785408     0.84   0.415    -.1036009    .2358551
         sex |   .0146742   .0131191     1.12   0.277    -.0127584    .0421067
         age |   .0000681   .0005229     0.13   0.898    -.0010278    .0011641
      single |   -.003043   .0186372    -0.16   0.873    -.0432187    .0371328
       divor |   .1162473   .0966528     1.20   0.236    -.0786442    .3111388
     norelig |   .0233279   .0369491     0.63   0.534    -.0532969    .0999528
     protest |   .0187339   .0265835     0.70   0.499    -.0416401     .079108
         com |   -.003286   .0295815    -0.11   0.912    -.0636921      .05712
        prof |  -.0080839   .0536765    -0.15   0.883    -.1236991    .1075314
     comform |   .1177046   .2118915     0.56   0.602    -.4222613    .6576705
    econfood |   .0041981   .0066623     0.63   0.539    -.0101813    .0185776
       house |  -.0389663   .0225516    -1.73   0.099    -.0858789    .0079464
        oven |   -.000865   .0391686    -0.02   0.983    -.0896901    .0879601
      lchang |    -.03254   .0526886    -0.62   0.550     -.149398     .084318
      llomue |  -.0116649   .0258922    -0.45   0.656    -.0649456    .0416158
     lchuabo |   .0484968    .025746     1.88   0.065    -.0031494    .1001431
    lchitewe |  -.0376926   .0220121    -1.71   0.097    -.0825453    .0071602
      lronga |   -.004696   .0226918    -0.21   0.838    -.0511637    .0417717
     chitsua |   .0709931   .1587486     0.45   0.667    -.2953072    .4372934
      living |  -.0031254   .0070329    -0.44   0.663    -.0179942    .0117434
       _cons |   .0565912   .0356363     1.59   0.128    -.0178953    .1310778
------------------------------------------------------------------------------

. estimates store dlakhama2

. 
. mi estimate, dots: mean dlakhama2 if control==1

Imputations (10):
  .........10 done

Multiple-imputation estimates      Imputations     =        10
Mean estimation                    Number of obs   =       452
                                   Average RVI     =    1.6002
                                   Largest FMI     =    0.6461
                                   Complete DF     =       451
DF adjustment:   Small sample      DF:     min     =     20.89
                                           avg     =     20.89
Within VCE type:     Analytic              max     =     20.89

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
   dlakhama2 |   .0318584   .0133158      .0041578     .059559
--------------------------------------------------------------

. matrix define aux=e(b_mi)

. scalar define m_dlakhama2=aux[1,1]

. display m_dlakhama2
.03185841

. 
. mi estimate (diff: _b[civiceduc]-_b[hotline]), saving(miest, replace): regress dlakhama2 civic
> educ hotline verdade pr1 pr2 pr3 post post_miss health health_miss police police_miss sex age 
> single divor norelig protest com prof comform econfood house oven lchang llomue lchuabo lchite
> we lronga chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     2.4282
                                                  Largest FMI     =     0.9502
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =       5.15
                                                          avg     =      20.09
                                                          max     =      63.42
Model F test:       Equal FMI                     F(  31,  109.7) =       0.38
Within VCE type:       Robust                     Prob > F        =     0.9986

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0128199   .0133199    -0.96   0.341    -.0397102    .0140704
     hotline |  -.0026767   .0168373    -0.16   0.875    -.0377107    .0323574
     verdade |  -.0023663   .0200242    -0.12   0.907    -.0446607     .039928
         pr1 |  -.0109128   .0205625    -0.53   0.597    -.0519983    .0301728
         pr2 |   .0351322   .0612314     0.57   0.580    -.1026015    .1728658
         pr3 |   .0113837   .0621188     0.18   0.859    -.1289013    .1516688
        post |  -.0199909   .0318761    -0.63   0.544    -.0906101    .0506283
   post_miss |   .0122905   .0498232     0.25   0.809    -.0963691      .12095
      health |    .005785   .0155293     0.37   0.714    -.0269694    .0385394
 health_miss |  -.0339183   .0483804    -0.70   0.500    -.1425532    .0747167
      police |  -.0072577   .0243232    -0.30   0.771    -.0606528    .0461374
 police_miss |   .0661271   .0785408     0.84   0.415    -.1036009    .2358551
         sex |   .0146742   .0131191     1.12   0.277    -.0127584    .0421067
         age |   .0000681   .0005229     0.13   0.898    -.0010278    .0011641
      single |   -.003043   .0186372    -0.16   0.873    -.0432187    .0371328
       divor |   .1162473   .0966528     1.20   0.236    -.0786442    .3111388
     norelig |   .0233279   .0369491     0.63   0.534    -.0532969    .0999528
     protest |   .0187339   .0265835     0.70   0.499    -.0416401     .079108
         com |   -.003286   .0295815    -0.11   0.912    -.0636921      .05712
        prof |  -.0080839   .0536765    -0.15   0.883    -.1236991    .1075314
     comform |   .1177046   .2118915     0.56   0.602    -.4222613    .6576705
    econfood |   .0041981   .0066623     0.63   0.539    -.0101813    .0185776
       house |  -.0389663   .0225516    -1.73   0.099    -.0858789    .0079464
        oven |   -.000865   .0391686    -0.02   0.983    -.0896901    .0879601
      lchang |    -.03254   .0526886    -0.62   0.550     -.149398     .084318
      llomue |  -.0116649   .0258922    -0.45   0.656    -.0649456    .0416158
     lchuabo |   .0484968    .025746     1.88   0.065    -.0031494    .1001431
    lchitewe |  -.0376926   .0220121    -1.71   0.097    -.0825453    .0071602
      lronga |   -.004696   .0226918    -0.21   0.838    -.0511637    .0417717
     chitsua |   .0709931   .1587486     0.45   0.667    -.2953072    .4372934
      living |  -.0031254   .0070329    -0.44   0.663    -.0179942    .0117434
       _cons |   .0565912   .0356363     1.59   0.128    -.0178953    .1310778
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.4522
                                                  Largest FMI     =     0.3291
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      50.09
                                                          avg     =      50.09
Within VCE type:       Robust                             max     =      50.09

         diff: _b[civiceduc]-_b[hotline]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |  -.0101433   .0121524    -0.83   0.408    -.0345511    .0142645
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[hotline]

 ( 1)  diff = 0

       F(  1,  50.1) =    0.70
            Prob > F =    0.4079

. scalar define t1_dlakhama2=r(p)

. display t1_dlakhama2
.40786632

. 
. mi estimate (diff: _b[civiceduc]-_b[verdade]), saving(miest, replace): regress dlakhama2 civic
> educ hotline verdade pr1 pr2 pr3 post post_miss health health_miss police police_miss sex age 
> single divor norelig protest com prof comform econfood house oven lchang llomue lchuabo lchite
> we lronga chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     2.4282
                                                  Largest FMI     =     0.9502
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =       5.15
                                                          avg     =      20.09
                                                          max     =      63.42
Model F test:       Equal FMI                     F(  31,  109.7) =       0.38
Within VCE type:       Robust                     Prob > F        =     0.9986

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0128199   .0133199    -0.96   0.341    -.0397102    .0140704
     hotline |  -.0026767   .0168373    -0.16   0.875    -.0377107    .0323574
     verdade |  -.0023663   .0200242    -0.12   0.907    -.0446607     .039928
         pr1 |  -.0109128   .0205625    -0.53   0.597    -.0519983    .0301728
         pr2 |   .0351322   .0612314     0.57   0.580    -.1026015    .1728658
         pr3 |   .0113837   .0621188     0.18   0.859    -.1289013    .1516688
        post |  -.0199909   .0318761    -0.63   0.544    -.0906101    .0506283
   post_miss |   .0122905   .0498232     0.25   0.809    -.0963691      .12095
      health |    .005785   .0155293     0.37   0.714    -.0269694    .0385394
 health_miss |  -.0339183   .0483804    -0.70   0.500    -.1425532    .0747167
      police |  -.0072577   .0243232    -0.30   0.771    -.0606528    .0461374
 police_miss |   .0661271   .0785408     0.84   0.415    -.1036009    .2358551
         sex |   .0146742   .0131191     1.12   0.277    -.0127584    .0421067
         age |   .0000681   .0005229     0.13   0.898    -.0010278    .0011641
      single |   -.003043   .0186372    -0.16   0.873    -.0432187    .0371328
       divor |   .1162473   .0966528     1.20   0.236    -.0786442    .3111388
     norelig |   .0233279   .0369491     0.63   0.534    -.0532969    .0999528
     protest |   .0187339   .0265835     0.70   0.499    -.0416401     .079108
         com |   -.003286   .0295815    -0.11   0.912    -.0636921      .05712
        prof |  -.0080839   .0536765    -0.15   0.883    -.1236991    .1075314
     comform |   .1177046   .2118915     0.56   0.602    -.4222613    .6576705
    econfood |   .0041981   .0066623     0.63   0.539    -.0101813    .0185776
       house |  -.0389663   .0225516    -1.73   0.099    -.0858789    .0079464
        oven |   -.000865   .0391686    -0.02   0.983    -.0896901    .0879601
      lchang |    -.03254   .0526886    -0.62   0.550     -.149398     .084318
      llomue |  -.0116649   .0258922    -0.45   0.656    -.0649456    .0416158
     lchuabo |   .0484968    .025746     1.88   0.065    -.0031494    .1001431
    lchitewe |  -.0376926   .0220121    -1.71   0.097    -.0825453    .0071602
      lronga |   -.004696   .0226918    -0.21   0.838    -.0511637    .0417717
     chitsua |   .0709931   .1587486     0.45   0.667    -.2953072    .4372934
      living |  -.0031254   .0070329    -0.44   0.663    -.0179942    .0117434
       _cons |   .0565912   .0356363     1.59   0.128    -.0178953    .1310778
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     1.0106
                                                  Largest FMI     =     0.5331
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      24.51
                                                          avg     =      24.51
Within VCE type:       Robust                             max     =      24.51

         diff: _b[civiceduc]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |  -.0104536   .0155386    -0.67   0.507    -.0424883     .021581
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[verdade]

 ( 1)  diff = 0

       F(  1,  24.5) =    0.45
            Prob > F =    0.5074

. scalar define t2_dlakhama2=r(p)

. display t2_dlakhama2
.50739958

. 
. mi estimate (diff: _b[hotline]-_b[verdade]), saving(miest, replace): regress dlakhama2 civiced
> uc hotline verdade pr1 pr2 pr3 post post_miss health health_miss police police_miss sex age si
> ngle divor norelig protest com prof comform econfood house oven lchang llomue lchuabo lchitewe
>  lronga chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     2.4282
                                                  Largest FMI     =     0.9502
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =       5.15
                                                          avg     =      20.09
                                                          max     =      63.42
Model F test:       Equal FMI                     F(  31,  109.7) =       0.38
Within VCE type:       Robust                     Prob > F        =     0.9986

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0128199   .0133199    -0.96   0.341    -.0397102    .0140704
     hotline |  -.0026767   .0168373    -0.16   0.875    -.0377107    .0323574
     verdade |  -.0023663   .0200242    -0.12   0.907    -.0446607     .039928
         pr1 |  -.0109128   .0205625    -0.53   0.597    -.0519983    .0301728
         pr2 |   .0351322   .0612314     0.57   0.580    -.1026015    .1728658
         pr3 |   .0113837   .0621188     0.18   0.859    -.1289013    .1516688
        post |  -.0199909   .0318761    -0.63   0.544    -.0906101    .0506283
   post_miss |   .0122905   .0498232     0.25   0.809    -.0963691      .12095
      health |    .005785   .0155293     0.37   0.714    -.0269694    .0385394
 health_miss |  -.0339183   .0483804    -0.70   0.500    -.1425532    .0747167
      police |  -.0072577   .0243232    -0.30   0.771    -.0606528    .0461374
 police_miss |   .0661271   .0785408     0.84   0.415    -.1036009    .2358551
         sex |   .0146742   .0131191     1.12   0.277    -.0127584    .0421067
         age |   .0000681   .0005229     0.13   0.898    -.0010278    .0011641
      single |   -.003043   .0186372    -0.16   0.873    -.0432187    .0371328
       divor |   .1162473   .0966528     1.20   0.236    -.0786442    .3111388
     norelig |   .0233279   .0369491     0.63   0.534    -.0532969    .0999528
     protest |   .0187339   .0265835     0.70   0.499    -.0416401     .079108
         com |   -.003286   .0295815    -0.11   0.912    -.0636921      .05712
        prof |  -.0080839   .0536765    -0.15   0.883    -.1236991    .1075314
     comform |   .1177046   .2118915     0.56   0.602    -.4222613    .6576705
    econfood |   .0041981   .0066623     0.63   0.539    -.0101813    .0185776
       house |  -.0389663   .0225516    -1.73   0.099    -.0858789    .0079464
        oven |   -.000865   .0391686    -0.02   0.983    -.0896901    .0879601
      lchang |    -.03254   .0526886    -0.62   0.550     -.149398     .084318
      llomue |  -.0116649   .0258922    -0.45   0.656    -.0649456    .0416158
     lchuabo |   .0484968    .025746     1.88   0.065    -.0031494    .1001431
    lchitewe |  -.0376926   .0220121    -1.71   0.097    -.0825453    .0071602
      lronga |   -.004696   .0226918    -0.21   0.838    -.0511637    .0417717
     chitsua |   .0709931   .1587486     0.45   0.667    -.2953072    .4372934
      living |  -.0031254   .0070329    -0.44   0.663    -.0179942    .0117434
       _cons |   .0565912   .0356363     1.59   0.128    -.0178953    .1310778
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.9095
                                                  Largest FMI     =     0.5054
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      26.82
                                                          avg     =      26.82
Within VCE type:       Robust                             max     =      26.82

         diff: _b[hotline]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
   dlakhama2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |  -.0003103   .0166493    -0.02   0.985    -.0344827    .0338621
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[hotline]-_b[verdade]

 ( 1)  diff = 0

       F(  1,  26.8) =    0.00
            Prob > F =    0.9853

. scalar define t3_dlakhama2=r(p)

. display t3_dlakhama2
.98526724

. 
. mi test civiceduc hotline verdade
note: assuming equal fractions of missing information

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,  55.0) =    0.34
            Prob > F =    0.7971

. scalar define t4_dlakhama2=r(p)

. display t4_dlakhama2
.79708086

. 
. mi estimate, dots: regress simango2 civiceduc hotline verdade pr1 pr2 pr3 post post_miss healt
> h health_miss police police_miss sex age single divor norelig protest com prof comform econfoo
> d house oven lchang llomue lchuabo lchitewe lronga chitsua living, cluster(ea)

Imputations (10):
  .........10 done

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     2.4220
                                                  Largest FMI     =     0.9573
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =       4.76
                                                          avg     =      23.72
                                                          max     =      71.96
Model F test:       Equal FMI                     F(  31,  109.9) =       0.49
Within VCE type:       Robust                     Prob > F        =     0.9880

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0026488   .0161954     0.16   0.871    -.0297897    .0350872
     hotline |  -.0019698    .015977    -0.12   0.902    -.0341717    .0302321
     verdade |  -.0171809    .014489    -1.19   0.240    -.0460644    .0117026
         pr1 |   .0374104   .0332838     1.12   0.275    -.0323925    .1072134
         pr2 |   .0503227   .0468493     1.07   0.296    -.0474198    .1480653
         pr3 |   .0498947   .0387842     1.29   0.206    -.0285752    .1283645
        post |   .0196716   .0297338     0.66   0.517     -.042928    .0822711
   post_miss |  -.0268076   .0216441    -1.24   0.227    -.0713621    .0177469
      health |  -.0211157    .018067    -1.17   0.261    -.0595906    .0173593
 health_miss |   .0135756   .1670545     0.08   0.939     -.422521    .4496723
      police |   .0041704   .0206755     0.20   0.843    -.0396583    .0479991
 police_miss |    .002332   .1046491     0.02   0.983    -.2309191    .2355832
         sex |   .0466346   .0165471     2.82   0.011     .0118621     .081407
         age |  -.0010135   .0005682    -1.78   0.089    -.0021979    .0001709
      single |  -.0177284    .017606    -1.01   0.326    -.0544995    .0190426
       divor |   .0225271   .1019446     0.22   0.829    -.2012904    .2463447
     norelig |   .0076843   .0425063     0.18   0.858    -.0793275    .0946961
     protest |   .0008358   .0195832     0.04   0.966    -.0402678    .0419393
         com |  -.0194418   .0264512    -0.74   0.470    -.0741076    .0352239
        prof |   .0902597   .0889247     1.02   0.323    -.0964269    .2769462
     comform |   .0028732   .1326309     0.02   0.983    -.3156492    .3213955
    econfood |   .0031392   .0058316     0.54   0.595    -.0088163    .0150947
       house |   .0017738   .0242881     0.07   0.943    -.0510644    .0546121
        oven |   .0281951   .0323307     0.87   0.393    -.0389286    .0953189
      lchang |  -.0225911   .0333849    -0.68   0.502    -.0900372     .044855
      llomue |  -.0165122   .0371719    -0.44   0.661    -.0933631    .0603387
     lchuabo |   .0173192   .0440805     0.39   0.701    -.0782237     .112862
    lchitewe |   .1647635   .1170046     1.41   0.168    -.0731468    .4026738
      lronga |  -.0139978   .0261151    -0.54   0.597    -.0682055    .0402099
     chitsua |  -.0553595    .031825    -1.74   0.091    -.1200515    .0093326
      living |   .0008729   .0083629     0.10   0.918    -.0168289    .0185747
       _cons |   .0411945   .0399103     1.03   0.319    -.0440912    .1264801
------------------------------------------------------------------------------

. estimates store simango2

. 
. mi estimate, dots: mean simango2 if control==1

Imputations (10):
  .........10 done

Multiple-imputation estimates      Imputations     =        10
Mean estimation                    Number of obs   =       452
                                   Average RVI     =    0.5448
                                   Largest FMI     =    0.3711
                                   Complete DF     =       451
DF adjustment:   Small sample      DF:     min     =     57.94
                                           avg     =     57.94
Within VCE type:     Analytic              max     =     57.94

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    simango2 |   .0440265   .0120008      .0200037    .0680494
--------------------------------------------------------------

. matrix define aux=e(b_mi)

. scalar define m_simango2=aux[1,1]

. display m_simango2
.04402655

. 
. mi estimate (diff: _b[civiceduc]-_b[hotline]), saving(miest, replace): regress simango2 civice
> duc hotline verdade pr1 pr2 pr3 post post_miss health health_miss police police_miss sex age s
> ingle divor norelig protest com prof comform econfood house oven lchang llomue lchuabo lchitew
> e lronga chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     2.4220
                                                  Largest FMI     =     0.9573
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =       4.76
                                                          avg     =      23.72
                                                          max     =      71.96
Model F test:       Equal FMI                     F(  31,  109.9) =       0.49
Within VCE type:       Robust                     Prob > F        =     0.9880

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0026488   .0161954     0.16   0.871    -.0297897    .0350872
     hotline |  -.0019698    .015977    -0.12   0.902    -.0341717    .0302321
     verdade |  -.0171809    .014489    -1.19   0.240    -.0460644    .0117026
         pr1 |   .0374104   .0332838     1.12   0.275    -.0323925    .1072134
         pr2 |   .0503227   .0468493     1.07   0.296    -.0474198    .1480653
         pr3 |   .0498947   .0387842     1.29   0.206    -.0285752    .1283645
        post |   .0196716   .0297338     0.66   0.517     -.042928    .0822711
   post_miss |  -.0268076   .0216441    -1.24   0.227    -.0713621    .0177469
      health |  -.0211157    .018067    -1.17   0.261    -.0595906    .0173593
 health_miss |   .0135756   .1670545     0.08   0.939     -.422521    .4496723
      police |   .0041704   .0206755     0.20   0.843    -.0396583    .0479991
 police_miss |    .002332   .1046491     0.02   0.983    -.2309191    .2355832
         sex |   .0466346   .0165471     2.82   0.011     .0118621     .081407
         age |  -.0010135   .0005682    -1.78   0.089    -.0021979    .0001709
      single |  -.0177284    .017606    -1.01   0.326    -.0544995    .0190426
       divor |   .0225271   .1019446     0.22   0.829    -.2012904    .2463447
     norelig |   .0076843   .0425063     0.18   0.858    -.0793275    .0946961
     protest |   .0008358   .0195832     0.04   0.966    -.0402678    .0419393
         com |  -.0194418   .0264512    -0.74   0.470    -.0741076    .0352239
        prof |   .0902597   .0889247     1.02   0.323    -.0964269    .2769462
     comform |   .0028732   .1326309     0.02   0.983    -.3156492    .3213955
    econfood |   .0031392   .0058316     0.54   0.595    -.0088163    .0150947
       house |   .0017738   .0242881     0.07   0.943    -.0510644    .0546121
        oven |   .0281951   .0323307     0.87   0.393    -.0389286    .0953189
      lchang |  -.0225911   .0333849    -0.68   0.502    -.0900372     .044855
      llomue |  -.0165122   .0371719    -0.44   0.661    -.0933631    .0603387
     lchuabo |   .0173192   .0440805     0.39   0.701    -.0782237     .112862
    lchitewe |   .1647635   .1170046     1.41   0.168    -.0731468    .4026738
      lronga |  -.0139978   .0261151    -0.54   0.597    -.0682055    .0402099
     chitsua |  -.0553595    .031825    -1.74   0.091    -.1200515    .0093326
      living |   .0008729   .0083629     0.10   0.918    -.0168289    .0185747
       _cons |   .0411945   .0399103     1.03   0.319    -.0440912    .1264801
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.2782
                                                  Largest FMI     =     0.2283
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      74.89
                                                          avg     =      74.89
Within VCE type:       Robust                             max     =      74.89

         diff: _b[civiceduc]-_b[hotline]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |   .0046185   .0147608     0.31   0.755    -.0247872    .0340243
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[hotline]

 ( 1)  diff = 0

       F(  1,  74.9) =    0.10
            Prob > F =    0.7552

. scalar define t1_simango2=r(p)

. display t1_simango2
.75523179

. 
. mi estimate (diff: _b[civiceduc]-_b[verdade]), saving(miest, replace): regress simango2 civice
> duc hotline verdade pr1 pr2 pr3 post post_miss health health_miss police police_miss sex age s
> ingle divor norelig protest com prof comform econfood house oven lchang llomue lchuabo lchitew
> e lronga chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     2.4220
                                                  Largest FMI     =     0.9573
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =       4.76
                                                          avg     =      23.72
                                                          max     =      71.96
Model F test:       Equal FMI                     F(  31,  109.9) =       0.49
Within VCE type:       Robust                     Prob > F        =     0.9880

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0026488   .0161954     0.16   0.871    -.0297897    .0350872
     hotline |  -.0019698    .015977    -0.12   0.902    -.0341717    .0302321
     verdade |  -.0171809    .014489    -1.19   0.240    -.0460644    .0117026
         pr1 |   .0374104   .0332838     1.12   0.275    -.0323925    .1072134
         pr2 |   .0503227   .0468493     1.07   0.296    -.0474198    .1480653
         pr3 |   .0498947   .0387842     1.29   0.206    -.0285752    .1283645
        post |   .0196716   .0297338     0.66   0.517     -.042928    .0822711
   post_miss |  -.0268076   .0216441    -1.24   0.227    -.0713621    .0177469
      health |  -.0211157    .018067    -1.17   0.261    -.0595906    .0173593
 health_miss |   .0135756   .1670545     0.08   0.939     -.422521    .4496723
      police |   .0041704   .0206755     0.20   0.843    -.0396583    .0479991
 police_miss |    .002332   .1046491     0.02   0.983    -.2309191    .2355832
         sex |   .0466346   .0165471     2.82   0.011     .0118621     .081407
         age |  -.0010135   .0005682    -1.78   0.089    -.0021979    .0001709
      single |  -.0177284    .017606    -1.01   0.326    -.0544995    .0190426
       divor |   .0225271   .1019446     0.22   0.829    -.2012904    .2463447
     norelig |   .0076843   .0425063     0.18   0.858    -.0793275    .0946961
     protest |   .0008358   .0195832     0.04   0.966    -.0402678    .0419393
         com |  -.0194418   .0264512    -0.74   0.470    -.0741076    .0352239
        prof |   .0902597   .0889247     1.02   0.323    -.0964269    .2769462
     comform |   .0028732   .1326309     0.02   0.983    -.3156492    .3213955
    econfood |   .0031392   .0058316     0.54   0.595    -.0088163    .0150947
       house |   .0017738   .0242881     0.07   0.943    -.0510644    .0546121
        oven |   .0281951   .0323307     0.87   0.393    -.0389286    .0953189
      lchang |  -.0225911   .0333849    -0.68   0.502    -.0900372     .044855
      llomue |  -.0165122   .0371719    -0.44   0.661    -.0933631    .0603387
     lchuabo |   .0173192   .0440805     0.39   0.701    -.0782237     .112862
    lchitewe |   .1647635   .1170046     1.41   0.168    -.0731468    .4026738
      lronga |  -.0139978   .0261151    -0.54   0.597    -.0682055    .0402099
     chitsua |  -.0553595    .031825    -1.74   0.091    -.1200515    .0093326
      living |   .0008729   .0083629     0.10   0.918    -.0168289    .0185747
       _cons |   .0411945   .0399103     1.03   0.319    -.0440912    .1264801
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.7330
                                                  Largest FMI     =     0.4488
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      32.42
                                                          avg     =      32.42
Within VCE type:       Robust                             max     =      32.42

         diff: _b[civiceduc]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |   .0198296    .016292     1.22   0.232    -.0133392    .0529985
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[verdade]

 ( 1)  diff = 0

       F(  1,  32.4) =    1.48
            Prob > F =    0.2323

. scalar define t2_simango2=r(p)

. display t2_simango2
.23233697

. 
. mi estimate (diff: _b[hotline]-_b[verdade]), saving(miest, replace): regress simango2 civicedu
> c hotline verdade pr1 pr2 pr3 post post_miss health health_miss police police_miss sex age sin
> gle divor norelig protest com prof comform econfood house oven lchang llomue lchuabo lchitewe 
> lronga chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     2.4220
                                                  Largest FMI     =     0.9573
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =       4.76
                                                          avg     =      23.72
                                                          max     =      71.96
Model F test:       Equal FMI                     F(  31,  109.9) =       0.49
Within VCE type:       Robust                     Prob > F        =     0.9880

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0026488   .0161954     0.16   0.871    -.0297897    .0350872
     hotline |  -.0019698    .015977    -0.12   0.902    -.0341717    .0302321
     verdade |  -.0171809    .014489    -1.19   0.240    -.0460644    .0117026
         pr1 |   .0374104   .0332838     1.12   0.275    -.0323925    .1072134
         pr2 |   .0503227   .0468493     1.07   0.296    -.0474198    .1480653
         pr3 |   .0498947   .0387842     1.29   0.206    -.0285752    .1283645
        post |   .0196716   .0297338     0.66   0.517     -.042928    .0822711
   post_miss |  -.0268076   .0216441    -1.24   0.227    -.0713621    .0177469
      health |  -.0211157    .018067    -1.17   0.261    -.0595906    .0173593
 health_miss |   .0135756   .1670545     0.08   0.939     -.422521    .4496723
      police |   .0041704   .0206755     0.20   0.843    -.0396583    .0479991
 police_miss |    .002332   .1046491     0.02   0.983    -.2309191    .2355832
         sex |   .0466346   .0165471     2.82   0.011     .0118621     .081407
         age |  -.0010135   .0005682    -1.78   0.089    -.0021979    .0001709
      single |  -.0177284    .017606    -1.01   0.326    -.0544995    .0190426
       divor |   .0225271   .1019446     0.22   0.829    -.2012904    .2463447
     norelig |   .0076843   .0425063     0.18   0.858    -.0793275    .0946961
     protest |   .0008358   .0195832     0.04   0.966    -.0402678    .0419393
         com |  -.0194418   .0264512    -0.74   0.470    -.0741076    .0352239
        prof |   .0902597   .0889247     1.02   0.323    -.0964269    .2769462
     comform |   .0028732   .1326309     0.02   0.983    -.3156492    .3213955
    econfood |   .0031392   .0058316     0.54   0.595    -.0088163    .0150947
       house |   .0017738   .0242881     0.07   0.943    -.0510644    .0546121
        oven |   .0281951   .0323307     0.87   0.393    -.0389286    .0953189
      lchang |  -.0225911   .0333849    -0.68   0.502    -.0900372     .044855
      llomue |  -.0165122   .0371719    -0.44   0.661    -.0933631    .0603387
     lchuabo |   .0173192   .0440805     0.39   0.701    -.0782237     .112862
    lchitewe |   .1647635   .1170046     1.41   0.168    -.0731468    .4026738
      lronga |  -.0139978   .0261151    -0.54   0.597    -.0682055    .0402099
     chitsua |  -.0553595    .031825    -1.74   0.091    -.1200515    .0093326
      living |   .0008729   .0083629     0.10   0.918    -.0168289    .0185747
       _cons |   .0411945   .0399103     1.03   0.319    -.0440912    .1264801
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.8319
                                                  Largest FMI     =     0.4819
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      28.98
                                                          avg     =      28.98
Within VCE type:       Robust                             max     =      28.98

         diff: _b[hotline]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
    simango2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |   .0152111   .0160524     0.95   0.351    -.0176206    .0480428
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[hotline]-_b[verdade]

 ( 1)  diff = 0

       F(  1,  29.0) =    0.90
            Prob > F =    0.3512

. scalar define t3_simango2=r(p)

. display t3_simango2
.3511722

. 
. mi test civiceduc hotline verdade
note: assuming equal fractions of missing information

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,  79.9) =    0.73
            Prob > F =    0.5369

. scalar define t4_simango2=r(p)

. display t4_simango2
.53691276

. 
. mi estimate, dots: regress frelimo2 civiceduc hotline verdade pr1 pr2 pr3 post post_miss healt
> h health_miss police police_miss sex age single divor norelig protest com prof comform econfoo
> d house oven lchang llomue lchuabo lchitewe lronga chitsua living, cluster(ea)

Imputations (10):
  .........10 done

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     1.0768
                                                  Largest FMI     =     0.7636
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      12.07
                                                          avg     =      32.83
                                                          max     =      85.27
Model F test:       Equal FMI                     F(  31,  131.9) =       1.61
Within VCE type:       Robust                     Prob > F        =     0.0348

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0319407   .0271537     1.18   0.243    -.0220457     .085927
     hotline |    .042801   .0311393     1.37   0.180    -.0209643    .1065663
     verdade |   .0118516   .0296034     0.40   0.690    -.0470062    .0707093
         pr1 |   -.132359   .0693103    -1.91   0.071    -.2772488    .0125309
         pr2 |  -.0776282   .0864876    -0.90   0.385    -.2631567    .1079003
         pr3 |  -.0031188   .0712243    -0.04   0.965    -.1491663    .1429287
        post |   .0111325   .0545608     0.20   0.841    -.1035215    .1257865
   post_miss |   .0055311   .0687087     0.08   0.936    -.1336533    .1447156
      health |    .006272   .0270908     0.23   0.819     -.049703    .0622469
 health_miss |   .1106454   .0902689     1.23   0.244    -.0859067    .3071975
      police |  -.0193243   .0407307    -0.47   0.640    -.1035603    .0649117
 police_miss |   -.216773   .1658306    -1.31   0.210    -.5693316    .1357855
         sex |   .0149695   .0223218     0.67   0.507    -.0303704    .0603094
         age |   -.000786   .0008695    -0.90   0.369    -.0025169    .0009449
      single |  -.0444976   .0344536    -1.29   0.210    -.1161104    .0271153
       divor |  -.0455135   .1377299    -0.33   0.744    -.3290163    .2379894
     norelig |  -.0820582   .0653115    -1.26   0.220    -.2160855    .0519691
     protest |  -.0413089   .0277773    -1.49   0.142    -.0967237    .0141059
         com |  -.0491713   .0653897    -0.75   0.460    -.1845418    .0861991
        prof |    .047103   .0897126     0.53   0.607    -.1440635    .2382696
     comform |  -.1121647   .0999555    -1.12   0.269    -.3148336    .0905043
    econfood |  -.0135796   .0096487    -1.41   0.165    -.0329279    .0057686
       house |   .0501585   .0350432     1.43   0.163    -.0214112    .1217282
        oven |   .0655297   .0402493     1.63   0.113    -.0164149    .1474743
      lchang |   .0039233   .0715136     0.05   0.957    -.1450403     .152887
      llomue |    .004747   .0665498     0.07   0.944    -.1317358    .1412299
     lchuabo |  -.0138228    .059813    -0.23   0.819    -.1368313    .1091856
    lchitewe |   .0006474   .1244759     0.01   0.996    -.2487371    .2500319
      lronga |  -.0311123   .0537403    -0.58   0.570    -.1438008    .0815762
     chitsua |   .0965835   .0898803     1.07   0.294    -.0900708    .2832377
      living |   .0309611   .0131949     2.35   0.027     .0038273    .0580949
       _cons |   .7991633   .0664622    12.02   0.000     .6612461    .9370805
------------------------------------------------------------------------------

. estimates store frelimo2

. 
. mi estimate, dots: mean frelimo2 if control==1

Imputations (10):
  .........10 done

Multiple-imputation estimates      Imputations     =        10
Mean estimation                    Number of obs   =       452
                                   Average RVI     =    1.2236
                                   Largest FMI     =    0.5795
                                   Complete DF     =       451
DF adjustment:   Small sample      DF:     min     =     25.91
                                           avg     =     25.91
Within VCE type:     Analytic              max     =     25.91

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    frelimo2 |   .8336283   .0261208      .7799271    .8873296
--------------------------------------------------------------

. matrix define aux=e(b_mi)

. scalar define m_frelimo2=aux[1,1]

. display m_frelimo2
.83362832

. 
. mi estimate (diff: _b[civiceduc]-_b[hotline]), saving(miest, replace): regress frelimo2 civice
> duc hotline verdade pr1 pr2 pr3 post post_miss health health_miss police police_miss sex age s
> ingle divor norelig protest com prof comform econfood house oven lchang llomue lchuabo lchitew
> e lronga chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     1.0768
                                                  Largest FMI     =     0.7636
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      12.07
                                                          avg     =      32.83
                                                          max     =      85.27
Model F test:       Equal FMI                     F(  31,  131.9) =       1.61
Within VCE type:       Robust                     Prob > F        =     0.0348

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0319407   .0271537     1.18   0.243    -.0220457     .085927
     hotline |    .042801   .0311393     1.37   0.180    -.0209643    .1065663
     verdade |   .0118516   .0296034     0.40   0.690    -.0470062    .0707093
         pr1 |   -.132359   .0693103    -1.91   0.071    -.2772488    .0125309
         pr2 |  -.0776282   .0864876    -0.90   0.385    -.2631567    .1079003
         pr3 |  -.0031188   .0712243    -0.04   0.965    -.1491663    .1429287
        post |   .0111325   .0545608     0.20   0.841    -.1035215    .1257865
   post_miss |   .0055311   .0687087     0.08   0.936    -.1336533    .1447156
      health |    .006272   .0270908     0.23   0.819     -.049703    .0622469
 health_miss |   .1106454   .0902689     1.23   0.244    -.0859067    .3071975
      police |  -.0193243   .0407307    -0.47   0.640    -.1035603    .0649117
 police_miss |   -.216773   .1658306    -1.31   0.210    -.5693316    .1357855
         sex |   .0149695   .0223218     0.67   0.507    -.0303704    .0603094
         age |   -.000786   .0008695    -0.90   0.369    -.0025169    .0009449
      single |  -.0444976   .0344536    -1.29   0.210    -.1161104    .0271153
       divor |  -.0455135   .1377299    -0.33   0.744    -.3290163    .2379894
     norelig |  -.0820582   .0653115    -1.26   0.220    -.2160855    .0519691
     protest |  -.0413089   .0277773    -1.49   0.142    -.0967237    .0141059
         com |  -.0491713   .0653897    -0.75   0.460    -.1845418    .0861991
        prof |    .047103   .0897126     0.53   0.607    -.1440635    .2382696
     comform |  -.1121647   .0999555    -1.12   0.269    -.3148336    .0905043
    econfood |  -.0135796   .0096487    -1.41   0.165    -.0329279    .0057686
       house |   .0501585   .0350432     1.43   0.163    -.0214112    .1217282
        oven |   .0655297   .0402493     1.63   0.113    -.0164149    .1474743
      lchang |   .0039233   .0715136     0.05   0.957    -.1450403     .152887
      llomue |    .004747   .0665498     0.07   0.944    -.1317358    .1412299
     lchuabo |  -.0138228    .059813    -0.23   0.819    -.1368313    .1091856
    lchitewe |   .0006474   .1244759     0.01   0.996    -.2487371    .2500319
      lronga |  -.0311123   .0537403    -0.58   0.570    -.1438008    .0815762
     chitsua |   .0965835   .0898803     1.07   0.294    -.0900708    .2832377
      living |   .0309611   .0131949     2.35   0.027     .0038273    .0580949
       _cons |   .7991633   .0664622    12.02   0.000     .6612461    .9370805
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.3733
                                                  Largest FMI     =     0.2865
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      59.17
                                                          avg     =      59.17
Within VCE type:       Robust                             max     =      59.17

         diff: _b[civiceduc]-_b[hotline]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |  -.0108603   .0258469    -0.42   0.676    -.0625768    .0408561
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[hotline]

 ( 1)  diff = 0

       F(  1,  59.2) =    0.18
            Prob > F =    0.6759

. scalar define t1_frelimo2=r(p)

. display t1_frelimo2
.67587649

. 
. mi estimate (diff: _b[civiceduc]-_b[verdade]), saving(miest, replace): regress frelimo2 civice
> duc hotline verdade pr1 pr2 pr3 post post_miss health health_miss police police_miss sex age s
> ingle divor norelig protest com prof comform econfood house oven lchang llomue lchuabo lchitew
> e lronga chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     1.0768
                                                  Largest FMI     =     0.7636
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      12.07
                                                          avg     =      32.83
                                                          max     =      85.27
Model F test:       Equal FMI                     F(  31,  131.9) =       1.61
Within VCE type:       Robust                     Prob > F        =     0.0348

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0319407   .0271537     1.18   0.243    -.0220457     .085927
     hotline |    .042801   .0311393     1.37   0.180    -.0209643    .1065663
     verdade |   .0118516   .0296034     0.40   0.690    -.0470062    .0707093
         pr1 |   -.132359   .0693103    -1.91   0.071    -.2772488    .0125309
         pr2 |  -.0776282   .0864876    -0.90   0.385    -.2631567    .1079003
         pr3 |  -.0031188   .0712243    -0.04   0.965    -.1491663    .1429287
        post |   .0111325   .0545608     0.20   0.841    -.1035215    .1257865
   post_miss |   .0055311   .0687087     0.08   0.936    -.1336533    .1447156
      health |    .006272   .0270908     0.23   0.819     -.049703    .0622469
 health_miss |   .1106454   .0902689     1.23   0.244    -.0859067    .3071975
      police |  -.0193243   .0407307    -0.47   0.640    -.1035603    .0649117
 police_miss |   -.216773   .1658306    -1.31   0.210    -.5693316    .1357855
         sex |   .0149695   .0223218     0.67   0.507    -.0303704    .0603094
         age |   -.000786   .0008695    -0.90   0.369    -.0025169    .0009449
      single |  -.0444976   .0344536    -1.29   0.210    -.1161104    .0271153
       divor |  -.0455135   .1377299    -0.33   0.744    -.3290163    .2379894
     norelig |  -.0820582   .0653115    -1.26   0.220    -.2160855    .0519691
     protest |  -.0413089   .0277773    -1.49   0.142    -.0967237    .0141059
         com |  -.0491713   .0653897    -0.75   0.460    -.1845418    .0861991
        prof |    .047103   .0897126     0.53   0.607    -.1440635    .2382696
     comform |  -.1121647   .0999555    -1.12   0.269    -.3148336    .0905043
    econfood |  -.0135796   .0096487    -1.41   0.165    -.0329279    .0057686
       house |   .0501585   .0350432     1.43   0.163    -.0214112    .1217282
        oven |   .0655297   .0402493     1.63   0.113    -.0164149    .1474743
      lchang |   .0039233   .0715136     0.05   0.957    -.1450403     .152887
      llomue |    .004747   .0665498     0.07   0.944    -.1317358    .1412299
     lchuabo |  -.0138228    .059813    -0.23   0.819    -.1368313    .1091856
    lchitewe |   .0006474   .1244759     0.01   0.996    -.2487371    .2500319
      lronga |  -.0311123   .0537403    -0.58   0.570    -.1438008    .0815762
     chitsua |   .0965835   .0898803     1.07   0.294    -.0900708    .2832377
      living |   .0309611   .0131949     2.35   0.027     .0038273    .0580949
       _cons |   .7991633   .0664622    12.02   0.000     .6612461    .9370805
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.1027
                                                  Largest FMI     =     0.0961
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =     125.91
                                                          avg     =     125.91
Within VCE type:       Robust                             max     =     125.91

         diff: _b[civiceduc]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |   .0200891   .0263613     0.76   0.447    -.0320795    .0722578
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[verdade]

 ( 1)  diff = 0

       F(  1, 125.9) =    0.58
            Prob > F =    0.4474

. scalar define t2_frelimo2=r(p)

. display t2_frelimo2
.44744461

. 
. mi estimate (diff: _b[hotline]-_b[verdade]), saving(miest, replace): regress frelimo2 civicedu
> c hotline verdade pr1 pr2 pr3 post post_miss health health_miss police police_miss sex age sin
> gle divor norelig protest com prof comform econfood house oven lchang llomue lchuabo lchitewe 
> lronga chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     1.0768
                                                  Largest FMI     =     0.7636
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      12.07
                                                          avg     =      32.83
                                                          max     =      85.27
Model F test:       Equal FMI                     F(  31,  131.9) =       1.61
Within VCE type:       Robust                     Prob > F        =     0.0348

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |   .0319407   .0271537     1.18   0.243    -.0220457     .085927
     hotline |    .042801   .0311393     1.37   0.180    -.0209643    .1065663
     verdade |   .0118516   .0296034     0.40   0.690    -.0470062    .0707093
         pr1 |   -.132359   .0693103    -1.91   0.071    -.2772488    .0125309
         pr2 |  -.0776282   .0864876    -0.90   0.385    -.2631567    .1079003
         pr3 |  -.0031188   .0712243    -0.04   0.965    -.1491663    .1429287
        post |   .0111325   .0545608     0.20   0.841    -.1035215    .1257865
   post_miss |   .0055311   .0687087     0.08   0.936    -.1336533    .1447156
      health |    .006272   .0270908     0.23   0.819     -.049703    .0622469
 health_miss |   .1106454   .0902689     1.23   0.244    -.0859067    .3071975
      police |  -.0193243   .0407307    -0.47   0.640    -.1035603    .0649117
 police_miss |   -.216773   .1658306    -1.31   0.210    -.5693316    .1357855
         sex |   .0149695   .0223218     0.67   0.507    -.0303704    .0603094
         age |   -.000786   .0008695    -0.90   0.369    -.0025169    .0009449
      single |  -.0444976   .0344536    -1.29   0.210    -.1161104    .0271153
       divor |  -.0455135   .1377299    -0.33   0.744    -.3290163    .2379894
     norelig |  -.0820582   .0653115    -1.26   0.220    -.2160855    .0519691
     protest |  -.0413089   .0277773    -1.49   0.142    -.0967237    .0141059
         com |  -.0491713   .0653897    -0.75   0.460    -.1845418    .0861991
        prof |    .047103   .0897126     0.53   0.607    -.1440635    .2382696
     comform |  -.1121647   .0999555    -1.12   0.269    -.3148336    .0905043
    econfood |  -.0135796   .0096487    -1.41   0.165    -.0329279    .0057686
       house |   .0501585   .0350432     1.43   0.163    -.0214112    .1217282
        oven |   .0655297   .0402493     1.63   0.113    -.0164149    .1474743
      lchang |   .0039233   .0715136     0.05   0.957    -.1450403     .152887
      llomue |    .004747   .0665498     0.07   0.944    -.1317358    .1412299
     lchuabo |  -.0138228    .059813    -0.23   0.819    -.1368313    .1091856
    lchitewe |   .0006474   .1244759     0.01   0.996    -.2487371    .2500319
      lronga |  -.0311123   .0537403    -0.58   0.570    -.1438008    .0815762
     chitsua |   .0965835   .0898803     1.07   0.294    -.0900708    .2832377
      living |   .0309611   .0131949     2.35   0.027     .0038273    .0580949
       _cons |   .7991633   .0664622    12.02   0.000     .6612461    .9370805
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.4681
                                                  Largest FMI     =     0.3371
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      48.58
                                                          avg     =      48.58
Within VCE type:       Robust                             max     =      48.58

         diff: _b[hotline]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
    frelimo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |   .0309495   .0295077     1.05   0.299    -.0283615    .0902604
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[hotline]-_b[verdade]

 ( 1)  diff = 0

       F(  1,  48.6) =    1.10
            Prob > F =    0.2994

. scalar define t3_frelimo2=r(p)

. display t3_frelimo2
.29943219

. 
. mi test civiceduc hotline verdade
note: assuming equal fractions of missing information

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,  94.4) =    1.00
            Prob > F =    0.3978

. scalar define t4_frelimo2=r(p)

. display t4_frelimo2
.39780888

. 
. mi estimate, dots: regress renamo2 civiceduc hotline verdade pr1 pr2 pr3 post post_miss health
>  health_miss police police_miss sex age single divor norelig protest com prof comform econfood
>  house oven lchang llomue lchuabo lchitewe lronga chitsua living, cluster(ea)

Imputations (10):
  .........10 done

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     2.0149
                                                  Largest FMI     =     0.8961
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =       7.43
                                                          avg     =      24.28
                                                          max     =      92.55
Model F test:       Equal FMI                     F(  31,  115.1) =       0.39
Within VCE type:       Robust                     Prob > F        =     0.9985

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0040567   .0127332    -0.32   0.751    -.0293439    .0212305
     hotline |   .0088388   .0146249     0.60   0.550    -.0209784     .038656
     verdade |  -.0036163   .0141831    -0.25   0.800     -.032503    .0252703
         pr1 |   .0044529   .0333725     0.13   0.896    -.0667651    .0756708
         pr2 |  -.0340066   .0378331    -0.90   0.382    -.1144101    .0463968
         pr3 |  -.0462148   .0382101    -1.21   0.243    -.1266383    .0342087
        post |  -.0200597   .0162222    -1.24   0.223    -.0527722    .0126528
   post_miss |  -.0156036   .0244744    -0.64   0.530    -.0663418    .0351346
      health |   .0034898   .0139047     0.25   0.804    -.0254998    .0324794
 health_miss |  -.0075245   .0441272    -0.17   0.868    -.1040694    .0890203
      police |  -.0011399   .0156193    -0.07   0.942    -.0331124    .0308326
 police_miss |   .0640502   .1113419     0.58   0.580    -.1911297      .31923
         sex |   .0112241   .0140591     0.80   0.435    -.0183718    .0408201
         age |   2.41e-06   .0003638     0.01   0.995    -.0007262     .000731
      single |  -.0104735    .015381    -0.68   0.504    -.0426814    .0217344
       divor |   .1228278   .0973156     1.26   0.211    -.0715657    .3172214
     norelig |   .0279433   .0343999     0.81   0.424    -.0430021    .0988888
     protest |   .0207717   .0127152     1.63   0.111    -.0050143    .0465578
         com |  -.0017955   .0281245    -0.06   0.950    -.0592931     .055702
        prof |   .0260391     .06072     0.43   0.673    -.1008598     .152938
     comform |  -.0081628   .0642822    -0.13   0.902    -.1558484    .1395228
    econfood |    .004338   .0065423     0.66   0.518    -.0096254    .0183013
       house |  -.0485227    .030124    -1.61   0.133    -.1141956    .0171502
        oven |   .0004545    .019002     0.02   0.981    -.0394452    .0403543
      lchang |   .0318596   .0388344     0.82   0.426    -.0517183    .1154374
      llomue |   .0150025   .0365793     0.41   0.687    -.0623076    .0923127
     lchuabo |   .0300952   .0336364     0.89   0.380    -.0395678    .0997583
    lchitewe |  -.0278733   .0554119    -0.50   0.623    -.1465435    .0907968
      lronga |  -.0189626   .0205462    -0.92   0.371    -.0629766    .0250514
     chitsua |    .019029   .1091161     0.17   0.866     -.235973     .274031
      living |  -.0035527   .0071023    -0.50   0.625    -.0188596    .0117542
       _cons |   .0629841   .0349343     1.80   0.088     -.010388    .1363562
------------------------------------------------------------------------------

. estimates store renamo2

. 
. mi estimate, dots: mean renamo2 if control==1

Imputations (10):
  .........10 done

Multiple-imputation estimates      Imputations     =        10
Mean estimation                    Number of obs   =       452
                                   Average RVI     =    0.8247
                                   Largest FMI     =    0.4768
                                   Complete DF     =       451
DF adjustment:   Small sample      DF:     min     =     37.37
                                           avg     =     37.37
Within VCE type:     Analytic              max     =     37.37

--------------------------------------------------------------
             |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
     renamo2 |   .0261062   .0101347      .0055781    .0466343
--------------------------------------------------------------

. matrix define aux=e(b_mi)

. scalar define m_renamo2=aux[1,1]

. display m_renamo2
.02610619

. 
. mi estimate (diff: _b[civiceduc]-_b[hotline]), saving(miest, replace): regress renamo2 civiced
> uc hotline verdade pr1 pr2 pr3 post post_miss health health_miss police police_miss sex age si
> ngle divor norelig protest com prof comform econfood house oven lchang llomue lchuabo lchitewe
>  lronga chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     2.0149
                                                  Largest FMI     =     0.8961
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =       7.43
                                                          avg     =      24.28
                                                          max     =      92.55
Model F test:       Equal FMI                     F(  31,  115.1) =       0.39
Within VCE type:       Robust                     Prob > F        =     0.9985

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0040567   .0127332    -0.32   0.751    -.0293439    .0212305
     hotline |   .0088388   .0146249     0.60   0.550    -.0209784     .038656
     verdade |  -.0036163   .0141831    -0.25   0.800     -.032503    .0252703
         pr1 |   .0044529   .0333725     0.13   0.896    -.0667651    .0756708
         pr2 |  -.0340066   .0378331    -0.90   0.382    -.1144101    .0463968
         pr3 |  -.0462148   .0382101    -1.21   0.243    -.1266383    .0342087
        post |  -.0200597   .0162222    -1.24   0.223    -.0527722    .0126528
   post_miss |  -.0156036   .0244744    -0.64   0.530    -.0663418    .0351346
      health |   .0034898   .0139047     0.25   0.804    -.0254998    .0324794
 health_miss |  -.0075245   .0441272    -0.17   0.868    -.1040694    .0890203
      police |  -.0011399   .0156193    -0.07   0.942    -.0331124    .0308326
 police_miss |   .0640502   .1113419     0.58   0.580    -.1911297      .31923
         sex |   .0112241   .0140591     0.80   0.435    -.0183718    .0408201
         age |   2.41e-06   .0003638     0.01   0.995    -.0007262     .000731
      single |  -.0104735    .015381    -0.68   0.504    -.0426814    .0217344
       divor |   .1228278   .0973156     1.26   0.211    -.0715657    .3172214
     norelig |   .0279433   .0343999     0.81   0.424    -.0430021    .0988888
     protest |   .0207717   .0127152     1.63   0.111    -.0050143    .0465578
         com |  -.0017955   .0281245    -0.06   0.950    -.0592931     .055702
        prof |   .0260391     .06072     0.43   0.673    -.1008598     .152938
     comform |  -.0081628   .0642822    -0.13   0.902    -.1558484    .1395228
    econfood |    .004338   .0065423     0.66   0.518    -.0096254    .0183013
       house |  -.0485227    .030124    -1.61   0.133    -.1141956    .0171502
        oven |   .0004545    .019002     0.02   0.981    -.0394452    .0403543
      lchang |   .0318596   .0388344     0.82   0.426    -.0517183    .1154374
      llomue |   .0150025   .0365793     0.41   0.687    -.0623076    .0923127
     lchuabo |   .0300952   .0336364     0.89   0.380    -.0395678    .0997583
    lchitewe |  -.0278733   .0554119    -0.50   0.623    -.1465435    .0907968
      lronga |  -.0189626   .0205462    -0.92   0.371    -.0629766    .0250514
     chitsua |    .019029   .1091161     0.17   0.866     -.235973     .274031
      living |  -.0035527   .0071023    -0.50   0.625    -.0188596    .0117542
       _cons |   .0629841   .0349343     1.80   0.088     -.010388    .1363562
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.3523
                                                  Largest FMI     =     0.2743
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      62.12
                                                          avg     =      62.12
Within VCE type:       Robust                             max     =      62.12

         diff: _b[civiceduc]-_b[hotline]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |  -.0128955   .0137057    -0.94   0.350    -.0402919    .0145009
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[hotline]

 ( 1)  diff = 0

       F(  1,  62.1) =    0.89
            Prob > F =    0.3504

. scalar define t1_renamo2=r(p)

. display t1_renamo2
.35041016

. 
. mi estimate (diff: _b[civiceduc]-_b[verdade]), saving(miest, replace): regress renamo2 civiced
> uc hotline verdade pr1 pr2 pr3 post post_miss health health_miss police police_miss sex age si
> ngle divor norelig protest com prof comform econfood house oven lchang llomue lchuabo lchitewe
>  lronga chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     2.0149
                                                  Largest FMI     =     0.8961
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =       7.43
                                                          avg     =      24.28
                                                          max     =      92.55
Model F test:       Equal FMI                     F(  31,  115.1) =       0.39
Within VCE type:       Robust                     Prob > F        =     0.9985

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0040567   .0127332    -0.32   0.751    -.0293439    .0212305
     hotline |   .0088388   .0146249     0.60   0.550    -.0209784     .038656
     verdade |  -.0036163   .0141831    -0.25   0.800     -.032503    .0252703
         pr1 |   .0044529   .0333725     0.13   0.896    -.0667651    .0756708
         pr2 |  -.0340066   .0378331    -0.90   0.382    -.1144101    .0463968
         pr3 |  -.0462148   .0382101    -1.21   0.243    -.1266383    .0342087
        post |  -.0200597   .0162222    -1.24   0.223    -.0527722    .0126528
   post_miss |  -.0156036   .0244744    -0.64   0.530    -.0663418    .0351346
      health |   .0034898   .0139047     0.25   0.804    -.0254998    .0324794
 health_miss |  -.0075245   .0441272    -0.17   0.868    -.1040694    .0890203
      police |  -.0011399   .0156193    -0.07   0.942    -.0331124    .0308326
 police_miss |   .0640502   .1113419     0.58   0.580    -.1911297      .31923
         sex |   .0112241   .0140591     0.80   0.435    -.0183718    .0408201
         age |   2.41e-06   .0003638     0.01   0.995    -.0007262     .000731
      single |  -.0104735    .015381    -0.68   0.504    -.0426814    .0217344
       divor |   .1228278   .0973156     1.26   0.211    -.0715657    .3172214
     norelig |   .0279433   .0343999     0.81   0.424    -.0430021    .0988888
     protest |   .0207717   .0127152     1.63   0.111    -.0050143    .0465578
         com |  -.0017955   .0281245    -0.06   0.950    -.0592931     .055702
        prof |   .0260391     .06072     0.43   0.673    -.1008598     .152938
     comform |  -.0081628   .0642822    -0.13   0.902    -.1558484    .1395228
    econfood |    .004338   .0065423     0.66   0.518    -.0096254    .0183013
       house |  -.0485227    .030124    -1.61   0.133    -.1141956    .0171502
        oven |   .0004545    .019002     0.02   0.981    -.0394452    .0403543
      lchang |   .0318596   .0388344     0.82   0.426    -.0517183    .1154374
      llomue |   .0150025   .0365793     0.41   0.687    -.0623076    .0923127
     lchuabo |   .0300952   .0336364     0.89   0.380    -.0395678    .0997583
    lchitewe |  -.0278733   .0554119    -0.50   0.623    -.1465435    .0907968
      lronga |  -.0189626   .0205462    -0.92   0.371    -.0629766    .0250514
     chitsua |    .019029   .1091161     0.17   0.866     -.235973     .274031
      living |  -.0035527   .0071023    -0.50   0.625    -.0188596    .0117542
       _cons |   .0629841   .0349343     1.80   0.088     -.010388    .1363562
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.5592
                                                  Largest FMI     =     0.3799
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      41.40
                                                          avg     =      41.40
Within VCE type:       Robust                             max     =      41.40

         diff: _b[civiceduc]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |  -.0004404   .0140157    -0.03   0.975    -.0287375    .0278567
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[civiceduc]-_b[verdade]

 ( 1)  diff = 0

       F(  1,  41.4) =    0.00
            Prob > F =    0.9751

. scalar define t2_renamo2=r(p)

. display t2_renamo2
.97508396

. 
. mi estimate (diff: _b[hotline]-_b[verdade]), saving(miest, replace): regress renamo2 civiceduc
>  hotline verdade pr1 pr2 pr3 post post_miss health health_miss police police_miss sex age sing
> le divor norelig protest com prof comform econfood house oven lchang llomue lchuabo lchitewe l
> ronga chitsua living, cluster(ea)

Multiple-imputation estimates                     Imputations     =         10
Linear regression                                 Number of obs   =       1766
                                                  Average RVI     =     2.0149
                                                  Largest FMI     =     0.8961
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =       7.43
                                                          avg     =      24.28
                                                          max     =      92.55
Model F test:       Equal FMI                     F(  31,  115.1) =       0.39
Within VCE type:       Robust                     Prob > F        =     0.9985

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   civiceduc |  -.0040567   .0127332    -0.32   0.751    -.0293439    .0212305
     hotline |   .0088388   .0146249     0.60   0.550    -.0209784     .038656
     verdade |  -.0036163   .0141831    -0.25   0.800     -.032503    .0252703
         pr1 |   .0044529   .0333725     0.13   0.896    -.0667651    .0756708
         pr2 |  -.0340066   .0378331    -0.90   0.382    -.1144101    .0463968
         pr3 |  -.0462148   .0382101    -1.21   0.243    -.1266383    .0342087
        post |  -.0200597   .0162222    -1.24   0.223    -.0527722    .0126528
   post_miss |  -.0156036   .0244744    -0.64   0.530    -.0663418    .0351346
      health |   .0034898   .0139047     0.25   0.804    -.0254998    .0324794
 health_miss |  -.0075245   .0441272    -0.17   0.868    -.1040694    .0890203
      police |  -.0011399   .0156193    -0.07   0.942    -.0331124    .0308326
 police_miss |   .0640502   .1113419     0.58   0.580    -.1911297      .31923
         sex |   .0112241   .0140591     0.80   0.435    -.0183718    .0408201
         age |   2.41e-06   .0003638     0.01   0.995    -.0007262     .000731
      single |  -.0104735    .015381    -0.68   0.504    -.0426814    .0217344
       divor |   .1228278   .0973156     1.26   0.211    -.0715657    .3172214
     norelig |   .0279433   .0343999     0.81   0.424    -.0430021    .0988888
     protest |   .0207717   .0127152     1.63   0.111    -.0050143    .0465578
         com |  -.0017955   .0281245    -0.06   0.950    -.0592931     .055702
        prof |   .0260391     .06072     0.43   0.673    -.1008598     .152938
     comform |  -.0081628   .0642822    -0.13   0.902    -.1558484    .1395228
    econfood |    .004338   .0065423     0.66   0.518    -.0096254    .0183013
       house |  -.0485227    .030124    -1.61   0.133    -.1141956    .0171502
        oven |   .0004545    .019002     0.02   0.981    -.0394452    .0403543
      lchang |   .0318596   .0388344     0.82   0.426    -.0517183    .1154374
      llomue |   .0150025   .0365793     0.41   0.687    -.0623076    .0923127
     lchuabo |   .0300952   .0336364     0.89   0.380    -.0395678    .0997583
    lchitewe |  -.0278733   .0554119    -0.50   0.623    -.1465435    .0907968
      lronga |  -.0189626   .0205462    -0.92   0.371    -.0629766    .0250514
     chitsua |    .019029   .1091161     0.17   0.866     -.235973     .274031
      living |  -.0035527   .0071023    -0.50   0.625    -.0188596    .0117542
       _cons |   .0629841   .0349343     1.80   0.088     -.010388    .1363562
------------------------------------------------------------------------------

Transformations                                   Average RVI     =     0.8212
                                                  Largest FMI     =     0.4785
                                                  Complete DF     =        160
DF adjustment:   Small sample                     DF:     min     =      29.31
                                                          avg     =      29.31
Within VCE type:       Robust                             max     =      29.31

         diff: _b[hotline]-_b[verdade]

                                  (Within VCE adjusted for 161 clusters in ea)
------------------------------------------------------------------------------
     renamo2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        diff |   .0124551   .0153041     0.81   0.422    -.0188308     .043741
------------------------------------------------------------------------------

. mi testtransform diff
note: assuming equal fractions of missing information

         diff: _b[hotline]-_b[verdade]

 ( 1)  diff = 0

       F(  1,  29.3) =    0.66
            Prob > F =    0.4223

. scalar define t3_renamo2=r(p)

. display t3_renamo2
.42229846

. 
. mi test civiceduc hotline verdade
note: assuming equal fractions of missing information

 ( 1)  civiceduc = 0
 ( 2)  hotline = 0
 ( 3)  verdade = 0

       F(  3,  74.6) =    0.33
            Prob > F =    0.8024

. scalar define t4_renamo2=r(p)

. display t4_renamo2
.80243774

. 
. global list1="$list1" + " tresp(b_mi V_mi)" + " tfinger(b_mi V_mi)" + " tseen(b_mi V_mi)" + " 
> intt(b_mi V_mi)" + " carta(b_mi V_mi)" + " guebas2(b_mi V_mi)" + " dlakhama2(b_mi V_mi)" + " s
> imango2(b_mi V_mi)" + " frelimo2(b_mi V_mi)" + " renamo2(b_mi V_mi)" 

.         
. matrix define means=(m_tresp, m_tfinger, m_tseen, m_intt, m_carta, m_guebas2, m_dlakhama2, m_s
> imango2, m_frelimo2, m_renamo2 \ t1_tresp, t1_tfinger, t1_tseen, t1_intt, t1_carta, t1_guebas2
> , t1_dlakhama2, t1_simango2, t1_frelimo2, t1_renamo2 \ t2_tresp, t2_tfinger, t2_tseen, t2_intt
> , t2_carta, t2_guebas2, t2_dlakhama2, t2_simango2, t2_frelimo2, t2_renamo2 \ t3_tresp, t3_tfin
> ger, t3_tseen, t3_intt, t3_carta, t3_guebas2, t3_dlakhama2, t3_simango2, t3_frelimo2, t3_renam
> o2 \ t4_tresp, t4_tfinger, t4_tseen, t4_intt, t4_carta, t4_guebas2, t4_dlakhama2, t4_simango2,
>  t4_frelimo2, t4_renamo2)

. global list2="$list2" + " means"

. 
. xml_tab $list1, below stats(r2_a N) nolabel save("attrition.xml") append sheet("mi") 


note: results saved to attrition.xml

. xml_tab $list2, save("attrition.xml") append sheet("mi stats") 


note: results saved to attrition.xml

. estimates clear

. 
. ************************************
. *****  OA TABLE 13: LEE BOUNDS *****
. ************************************
. 
. clear all

. set more off

. 
. use mozdata_aux, replace

. 
. global out="tresp tfinger tseen intt carta guebas2 dlakhama2 simango2 frelimo2 renamo2"

. 
. capture gen idrops2=.

. replace idrops2=1 if drops2==0
(2328 real changes made)

. replace idrops2=0 if drops2==1
(1204 real changes made)

. 
. sum idrops2 if time==1 & control==1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     idrops2 |       452    .6393805    .4807123          0          1

. scalar define n_control=r(N)

. *452
. sum idrops2 if time==1 & civiceduc==1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     idrops2 |       449    .6636971    .4729709          0          1

. scalar define n_civiceduc=r(N)

. *449
. sum idrops2 if time==1 & hotline==1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     idrops2 |       436    .6834862    .4656502          0          1

. scalar define n_hotline=r(N)

. *436
. sum idrops2 if time==1 & verdade==1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     idrops2 |       429    .6503497    .4774163          0          1

. scalar define n_verdade=r(N)

. *429
. 
. foreach i in $out {
  2. 
.         global list1=""
  3. 
.         capture gen `i'_control=`i' if time==1 & control==1
  4.         capture gen `i'_civiceduc=`i' if time==1 & civiceduc==1
  5.         capture gen `i'_hotline=`i' if time==1 & hotline==1
  6.         capture gen `i'_verdade=`i' if time==1 & verdade==1
  7. 
.         sum `i'_control
  8.         scalar define m_`i'_control=r(mean)
  9. 
.         sum `i'_civiceduc
 10.         scalar define m_`i'_civiceduc=r(mean)
 11.         scalar define m_`i'_1=m_`i'_civiceduc-m_`i'_control
 12.         ttest `i'_civiceduc=`i'_control, unpaired
 13.         scalar define se_`i'_1=r(se)
 14.         scalar define p_`i'_1=r(p)
 15.         leebounds `i' civiceduc if time==1 & hotline==0 & verdade==0, select(idrops2)
 16.         estimates store `i'_1
 17.         sum `i' if e(sample)==1 & control==1
 18.         scalar define n_`i'_1_1=r(N)/n_control
 19.         sum `i' if e(sample)==1 & civiceduc==1
 20.         scalar define n_`i'_1_2=r(N)/n_civiceduc
 21. 
.         sum `i'_hotline
 22.         scalar define m_`i'_hotline=r(mean)
 23.         scalar define m_`i'_2=m_`i'_hotline-m_`i'_control
 24.         ttest `i'_hotline=`i'_control, unpaired
 25.         scalar define se_`i'_2=r(se)
 26.         scalar define p_`i'_2=r(p)
 27.         leebounds `i' hotline if time==1 & civiceduc==0 & verdade==0, select(idrops2)
 28.         estimates store `i'_2
 29.         sum `i' if e(sample)==1 & control==1
 30.         scalar define n_`i'_2_1=r(N)/n_control
 31.         sum `i' if e(sample)==1 & hotline==1
 32.         scalar define n_`i'_2_2=r(N)/n_hotline
 33. 
.         sum `i'_verdade
 34.         scalar define m_`i'_verdade=r(mean)
 35.         scalar define m_`i'_3=m_`i'_verdade-m_`i'_control
 36.         ttest `i'_verdade=`i'_control, unpaired
 37.         scalar define se_`i'_3=r(se)
 38.         scalar define p_`i'_3=r(p)
 39.         leebounds `i' verdade if time==1 & civiceduc==0 & hotline==0, select(idrops2)
 40.         estimates store `i'_3
 41.         sum `i' if e(sample)==1 & control==1
 42.         scalar define n_`i'_3_1=r(N)/n_control
 43.         sum `i' if e(sample)==1 & verdade==1
 44.         scalar define n_`i'_3_2=r(N)/n_verdade
 45.         
.         global list1="$list1" + " `i'_1" + " `i'_2" + " `i'_3"
 46. 
.         xml_tab $list1, below stats(N Nsel) nolabel save("attrition.xml") append sheet("lee `i
> '") 
 47.         estimates clear
 48. 
. }

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
tresp_cont~l |       269    .8773234    .3286771          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
tresp_civi~c |       293    .9146758    .2798415          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
tresp_~c |     293    .9146758    .0163485    .2798415    .8824999    .9468516
tresp_~l |     269    .8773234    .0200398    .3286771     .837868    .9167789
---------+--------------------------------------------------------------------
combined |     562    .8967972    .0128443    .3044947    .8715683     .922026
---------+--------------------------------------------------------------------
    diff |            .0373523    .0256866               -.0131015    .0878062
------------------------------------------------------------------------------
    diff = mean(tresp_civiceduc) - mean(tresp_control)            t =   1.4542
Ho: diff = 0                                     degrees of freedom =      560

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9268         Pr(|T| > |t|) = 0.1465          Pr(T > t) = 0.0732

Lee (2009) treatment effect bounds

Number of obs.                     =   876
Number of selected obs.            =   562
Trimming porportion                =   0.0564

------------------------------------------------------------------------------
       tresp |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
civiceduc    |
       lower |   .0322516   .0268647     1.20   0.230    -.0204023    .0849055
       upper |   .0920326   .0557623     1.65   0.099    -.0172595    .2013246
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tresp |       269    .8773234    .3286771          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tresp |       293    .9146758    .2798415          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
tresp_hotl~e |       289    .9515571    .2150727          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
tresp~ne |     289    .9515571    .0126513    .2150727    .9266563    .9764579
tresp_~l |     269    .8773234    .0200398    .3286771     .837868    .9167789
---------+--------------------------------------------------------------------
combined |     558    .9157706    .0117679    .2779807    .8926558    .9388854
---------+--------------------------------------------------------------------
    diff |            .0742337    .0233608                .0283474      .12012
------------------------------------------------------------------------------
    diff = mean(tresp_hotline) - mean(tresp_control)              t =   3.1777
Ho: diff = 0                                     degrees of freedom =      556

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9992         Pr(|T| > |t|) = 0.0016          Pr(T > t) = 0.0008

Lee (2009) treatment effect bounds

Number of obs.                     =   859
Number of selected obs.            =   558
Trimming porportion                =   0.0800

------------------------------------------------------------------------------
       tresp |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hotline      |
       lower |   .0700225    .024434     2.87   0.004     .0221328    .1179123
       upper |   .1226766   .0200398     6.12   0.000     .0833993    .1619539
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tresp |       269    .8773234    .3286771          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tresp |       289    .9515571    .2150727          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
tresp_verd~e |       270    .9074074    .2903989          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
tresp~de |     270    .9074074    .0176731    .2903989    .8726122    .9422026
tresp_~l |     269    .8773234    .0200398    .3286771     .837868    .9167789
---------+--------------------------------------------------------------------
combined |     539    .8923933      .01336    .3101709    .8661492    .9186375
---------+--------------------------------------------------------------------
    diff |             .030084    .0267134               -.0223916    .0825596
------------------------------------------------------------------------------
    diff = mean(tresp_verdade) - mean(tresp_control)              t =   1.1262
Ho: diff = 0                                     degrees of freedom =      537

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.8697         Pr(|T| > |t|) = 0.2606          Pr(T > t) = 0.1303

Lee (2009) treatment effect bounds

Number of obs.                     =   852
Number of selected obs.            =   539
Trimming porportion                =   0.0314

------------------------------------------------------------------------------
       tresp |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
verdade      |
       lower |   .0270844   .0275349     0.98   0.325    -.0268829    .0810518
       upper |   .0594796   .0559024     1.06   0.287    -.0500872    .1690463
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tresp |       269    .8773234    .3286771          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tresp |       270    .9074074    .2903989          0          1


note: results saved to attrition.xml

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
tfinger_co~l |       269    .8066914    .3956289          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
tfinger_ci~c |       293    .8634812     .343926          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
tfinge~c |     293    .8634812    .0200924     .343926     .823937    .9030255
tfinge~l |     269    .8066914    .0241219    .3956289    .7591989     .854184
---------+--------------------------------------------------------------------
combined |     562    .8362989    .0156216    .3703337     .805615    .8669829
---------+--------------------------------------------------------------------
    diff |            .0567898    .0312075               -.0045082    .1180878
------------------------------------------------------------------------------
    diff = mean(tfinger_civice~c) - mean(tfinger_control)         t =   1.8197
Ho: diff = 0                                     degrees of freedom =      560

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9653         Pr(|T| > |t|) = 0.0693          Pr(T > t) = 0.0347

Lee (2009) treatment effect bounds

Number of obs.                     =   876
Number of selected obs.            =   562
Trimming porportion                =   0.0564

------------------------------------------------------------------------------
     tfinger |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
civiceduc    |
       lower |   .0486286   .0329756     1.47   0.140    -.0160024    .1132596
       upper |   .1084095   .0563944     1.92   0.055    -.0021215    .2189405
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     tfinger |       269    .8066914    .3956289          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     tfinger |       293    .8634812     .343926          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
tfinger_ho~e |       289    .8754325    .3308006          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
tfing~ne |     289    .8754325    .0194589    .3308006    .8371329    .9137321
tfinge~l |     269    .8066914    .0241219    .3956289    .7591989     .854184
---------+--------------------------------------------------------------------
combined |     558    .8422939    .0154429    .3647922    .8119605    .8726273
---------+--------------------------------------------------------------------
    diff |            .0687411    .0307957                .0082509    .1292313
------------------------------------------------------------------------------
    diff = mean(tfinger_hotline) - mean(tfinger_control)          t =   2.2322
Ho: diff = 0                                     degrees of freedom =      556

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9870         Pr(|T| > |t|) = 0.0260          Pr(T > t) = 0.0130

Lee (2009) treatment effect bounds

Number of obs.                     =   859
Number of selected obs.            =   558
Trimming porportion                =   0.0800

------------------------------------------------------------------------------
     tfinger |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hotline      |
       lower |   .0579125     .03277     1.77   0.077    -.0063156    .1221405
       upper |   .1448421    .057534     2.52   0.012     .0320775    .2576067
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     tfinger |       269    .8066914    .3956289          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     tfinger |       289    .8754325    .3308006          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
tfinger_ve~e |       270    .8444444    .3631065          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
tfing~de |     270    .8444444     .022098    .3631065    .8009375    .8879514
tfinge~l |     269    .8066914    .0241219    .3956289    .7591989     .854184
---------+--------------------------------------------------------------------
combined |     539     .825603    .0163593     .379803    .7934671    .8577389
---------+--------------------------------------------------------------------
    diff |             .037753    .0327085               -.0264993    .1020053
------------------------------------------------------------------------------
    diff = mean(tfinger_verdade) - mean(tfinger_control)          t =   1.1542
Ho: diff = 0                                     degrees of freedom =      537

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.8755         Pr(|T| > |t|) = 0.2489          Pr(T > t) = 0.1245

Lee (2009) treatment effect bounds

Number of obs.                     =   852
Number of selected obs.            =   539
Trimming porportion                =   0.0314

------------------------------------------------------------------------------
     tfinger |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
verdade      |
       lower |   .0327138   .0342155     0.96   0.339    -.0343474    .0997749
       upper |   .0651089   .0563189     1.16   0.248    -.0452741    .1754919
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     tfinger |       269    .8066914    .3956289          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     tfinger |       270    .8444444    .3631065          0          1


note: results saved to attrition.xml

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
tseen_cont~l |       269    .2379182    .4266025          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
tseen_civi~c |       293    .2901024    .4545858          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
tseen_~c |     293    .2901024    .0265572    .4545858    .2378346    .3423702
tseen_~l |     269    .2379182    .0260104    .4266025    .1867075     .289129
---------+--------------------------------------------------------------------
combined |     562    .2651246    .0186359    .4417927    .2285199    .3017292
---------+--------------------------------------------------------------------
    diff |            .0521842     .037274               -.0210297     .125398
------------------------------------------------------------------------------
    diff = mean(tseen_civiceduc) - mean(tseen_control)            t =   1.4000
Ho: diff = 0                                     degrees of freedom =      560

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9190         Pr(|T| > |t|) = 0.1621          Pr(T > t) = 0.0810

Lee (2009) treatment effect bounds

Number of obs.                     =   876
Number of selected obs.            =   562
Trimming porportion                =   0.0564

------------------------------------------------------------------------------
       tseen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
civiceduc    |
       lower |   .0097458    .054008     0.18   0.857    -.0961079    .1155995
       upper |   .0695268   .0413312     1.68   0.093    -.0114808    .1505343
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tseen |       269    .2379182    .4266025          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tseen |       293    .2901024    .4545858          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
tseen_hotl~e |       289    .3183391    .4666399          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
tseen~ne |     289    .3183391    .0274494    .4666399    .2643122     .372366
tseen_~l |     269    .2379182    .0260104    .4266025    .1867075     .289129
---------+--------------------------------------------------------------------
combined |     558    .2795699    .0190158    .4491906    .2422185    .3169212
---------+--------------------------------------------------------------------
    diff |            .0804209    .0379372                .0059032    .1549386
------------------------------------------------------------------------------
    diff = mean(tseen_hotline) - mean(tseen_control)              t =   2.1198
Ho: diff = 0                                     degrees of freedom =      556

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9828         Pr(|T| > |t|) = 0.0345          Pr(T > t) = 0.0172

Lee (2009) treatment effect bounds

Number of obs.                     =   859
Number of selected obs.            =   558
Trimming porportion                =   0.0800

------------------------------------------------------------------------------
       tseen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hotline      |
       lower |   .0211643    .054291     0.39   0.697    -.0852441    .1275728
       upper |    .108094   .0431908     2.50   0.012     .0234417    .1927463
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tseen |       269    .2379182    .4266025          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tseen |       289    .3183391    .4666399          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
tseen_verd~e |       270    .3148148    .4653046          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
tseen~de |     270    .3148148    .0283175    .4653046    .2590626     .370567
tseen_~l |     269    .2379182    .0260104    .4266025    .1867075     .289129
---------+--------------------------------------------------------------------
combined |     539    .2764378    .0192817    .4476514    .2385612    .3143145
---------+--------------------------------------------------------------------
    diff |            .0768966    .0384565                 .001353    .1524402
------------------------------------------------------------------------------
    diff = mean(tseen_verdade) - mean(tseen_control)              t =   1.9996
Ho: diff = 0                                     degrees of freedom =      537

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9770         Pr(|T| > |t|) = 0.0460          Pr(T > t) = 0.0230

Lee (2009) treatment effect bounds

Number of obs.                     =   852
Number of selected obs.            =   539
Trimming porportion                =   0.0314

------------------------------------------------------------------------------
       tseen |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
verdade      |
       lower |   .0546999   .0537756     1.02   0.309    -.0506984    .1600983
       upper |   .0870951    .042614     2.04   0.041     .0035732    .1706169
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tseen |       269    .2379182    .4266025          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       tseen |       270    .3148148    .4653046          0          1


note: results saved to attrition.xml

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
intt_control |       269    .7541158    .3534014          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
intt_civic~c |       293    .8205753     .310541          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
intt_c~c |     293    .8205753     .018142     .310541    .7848697     .856281
intt_c~l |     269    .7541158    .0215473    .3534014    .7116923    .7965392
---------+--------------------------------------------------------------------
combined |     562    .7887646    .0140514    .3331101    .7611648    .8163644
---------+--------------------------------------------------------------------
    diff |            .0664596    .0280131                .0114359    .1214832
------------------------------------------------------------------------------
    diff = mean(intt_civiceduc) - mean(intt_control)              t =   2.3724
Ho: diff = 0                                     degrees of freedom =      560

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9910         Pr(|T| > |t|) = 0.0180          Pr(T > t) = 0.0090

Lee (2009) treatment effect bounds

Number of obs.                     =   876
Number of selected obs.            =   562
Trimming porportion                =   0.0564

------------------------------------------------------------------------------
        intt |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
civiceduc    |
       lower |   .0557334   .0304199     1.83   0.067    -.0038885    .1153552
       upper |   .1155143   .0526427     2.19   0.028     .0123366    .2186921
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
        intt |       269    .7541158    .3534014          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
        intt |       293    .8205753     .310541          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
intt_hotline |       289    .8482452    .2744401          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
intt_h~e |     289    .8482452    .0161435    .2744401    .8164709    .8800195
intt_c~l |     269    .7541158    .0215473    .3534014    .7116923    .7965392
---------+--------------------------------------------------------------------
combined |     558    .8028674    .0134705    .3182003    .7764082    .8293266
---------+--------------------------------------------------------------------
    diff |            .0941294    .0266856                .0417125    .1465463
------------------------------------------------------------------------------
    diff = mean(intt_hotline) - mean(intt_control)                t =   3.5273
Ho: diff = 0                                     degrees of freedom =      556

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9998         Pr(|T| > |t|) = 0.0005          Pr(T > t) = 0.0002

Lee (2009) treatment effect bounds

Number of obs.                     =   859
Number of selected obs.            =   558
Trimming porportion                =   0.0800

------------------------------------------------------------------------------
        intt |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hotline      |
       lower |   .0809374   .0289776     2.79   0.005     .0241424    .1377325
       upper |   .1634469   .0408526     4.00   0.000     .0833772    .2435166
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
        intt |       269    .7541158    .3534014          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
        intt |       289    .8482452    .2744401          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
intt_verdade |       270    .8100529    .3251657          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
intt_v~e |     270    .8100529     .019789    .3251657     .771092    .8490138
intt_c~l |     269    .7541158    .0215473    .3534014    .7116923    .7965392
---------+--------------------------------------------------------------------
combined |     539    .7821362    .0146616    .3403883    .7533353    .8109372
---------+--------------------------------------------------------------------
    diff |            .0559371     .029251               -.0015233    .1133976
------------------------------------------------------------------------------
    diff = mean(intt_verdade) - mean(intt_control)                t =   1.9123
Ho: diff = 0                                     degrees of freedom =      537

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9718         Pr(|T| > |t|) = 0.0564          Pr(T > t) = 0.0282

Lee (2009) treatment effect bounds

Number of obs.                     =   852
Number of selected obs.            =   539
Trimming porportion                =   0.0314

------------------------------------------------------------------------------
        intt |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
verdade      |
       lower |   .0497838   .0313836     1.59   0.113     -.011727    .1112945
       upper |   .0821789   .0527849     1.56   0.120    -.0212776    .1856354
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
        intt |       269    .7541158    .3534014          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
        intt |       270    .8100529    .3251657          0          1


note: results saved to attrition.xml

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
carta_cont~l |       275    .1527273    .3603802          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
carta_civi~c |       298    .2080537    .4065982          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
carta_~c |     298    .2080537    .0235536    .4065982    .1617006    .2544068
carta_~l |     275    .1527273    .0217317    .3603802    .1099449    .1955097
---------+--------------------------------------------------------------------
combined |     573    .1815009    .0161157    .3857694    .1498476    .2131541
---------+--------------------------------------------------------------------
    diff |            .0553264    .0322026               -.0079236    .1185764
------------------------------------------------------------------------------
    diff = mean(carta_civiceduc) - mean(carta_control)            t =   1.7181
Ho: diff = 0                                     degrees of freedom =      571

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9568         Pr(|T| > |t|) = 0.0863          Pr(T > t) = 0.0432

Lee (2009) treatment effect bounds

Number of obs.                     =   887
Number of selected obs.            =   573
Trimming porportion                =   0.0540

------------------------------------------------------------------------------
       carta |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
civiceduc    |
       lower |   .0101154   .0531924     0.19   0.849    -.0941398    .1143706
       upper |   .0672039   .0347873     1.93   0.053    -.0009779    .1353857
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       carta |       275    .1527273    .3603802          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       carta |       298    .2080537    .4065982          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
carta_hotl~e |       296        .125     .331279          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
carta~ne |     296        .125    .0192552     .331279     .087105     .162895
carta_~l |     275    .1527273    .0217317    .3603802    .1099449    .1955097
---------+--------------------------------------------------------------------
combined |     571    .1383538    .0144618    .3455736    .1099488    .1667587
---------+--------------------------------------------------------------------
    diff |           -.0277273    .0289453               -.0845799    .0291254
------------------------------------------------------------------------------
    diff = mean(carta_hotline) - mean(carta_control)              t =  -0.9579
Ho: diff = 0                                     degrees of freedom =      569

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1693         Pr(|T| > |t|) = 0.3385          Pr(T > t) = 0.8307

Lee (2009) treatment effect bounds

Number of obs.                     =   872
Number of selected obs.            =   571
Trimming porportion                =   0.0794

------------------------------------------------------------------------------
       carta |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hotline      |
       lower |  -.1032258   .0556903    -1.85   0.064    -.2123767    .0059251
       upper |  -.0169418   .0308716    -0.55   0.583    -.0774489    .0435654
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       carta |       275    .1527273    .3603802          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       carta |       296        .125     .331279          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
carta_verd~e |       278    .2338129    .4240179          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
carta~de |     278    .2338129    .0254309    .4240179    .1837506    .2838753
carta_~l |     275    .1527273    .0217317    .3603802    .1099449    .1955097
---------+--------------------------------------------------------------------
combined |     553    .1934901    .0168138    .3953914    .1604633    .2265168
---------+--------------------------------------------------------------------
    diff |            .0810857    .0334808                .0153201    .1468512
------------------------------------------------------------------------------
    diff = mean(carta_verdade) - mean(carta_control)              t =   2.4219
Ho: diff = 0                                     degrees of freedom =      551

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9921         Pr(|T| > |t|) = 0.0158          Pr(T > t) = 0.0079

Lee (2009) treatment effect bounds

Number of obs.                     =   866
Number of selected obs.            =   553
Trimming porportion                =   0.0334

------------------------------------------------------------------------------
       carta |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
verdade      |
       lower |   .0546304    .052955     1.03   0.302    -.0491594    .1584202
       upper |   .0891589   .0362613     2.46   0.014     .0180881    .1602297
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       carta |       275    .1527273    .3603802          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       carta |       278    .2338129    .4240179          0          1


note: results saved to attrition.xml

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
guebas2_co~l |       249    .8192771    .3855634          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
guebas2_ci~c |       272    .8639706    .3434517          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
guebas~c |     272    .8639706    .0208248    .3434517    .8229716    .9049696
guebas~l |     249    .8192771    .0244341    .3855634    .7711523    .8674019
---------+--------------------------------------------------------------------
combined |     521    .8426104    .0159698    .3645177    .8112371    .8739836
---------+--------------------------------------------------------------------
    diff |            .0446935    .0319414               -.0180568    .1074438
------------------------------------------------------------------------------
    diff = mean(guebas2_civice~c) - mean(guebas2_control)         t =   1.3992
Ho: diff = 0                                     degrees of freedom =      519

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9188         Pr(|T| > |t|) = 0.1623          Pr(T > t) = 0.0812

Lee (2009) treatment effect bounds

Number of obs.                     =   835
Number of selected obs.            =   521
Trimming porportion                =   0.0601

------------------------------------------------------------------------------
     guebas2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
civiceduc    |
       lower |   .0359927   .0338662     1.06   0.288    -.0303838    .1023692
       upper |   .0999554   .0594785     1.68   0.093    -.0166204    .2165312
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     guebas2 |       249    .8192771    .3855634          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     guebas2 |       272    .8639706    .3434517          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
guebas2_ho~e |       262    .8740458    .3324328          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
gueba~ne |     262    .8740458    .0205378    .3324328     .833605    .9144866
guebas~l |     249    .8192771    .0244341    .3855634    .7711523    .8674019
---------+--------------------------------------------------------------------
combined |     511    .8473581    .0159252    .3599944     .816071    .8786452
---------+--------------------------------------------------------------------
    diff |            .0547687    .0317995               -.0077057    .1172431
------------------------------------------------------------------------------
    diff = mean(guebas2_hotline) - mean(guebas2_control)          t =   1.7223
Ho: diff = 0                                     degrees of freedom =      509

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9572         Pr(|T| > |t|) = 0.0856          Pr(T > t) = 0.0428

Lee (2009) treatment effect bounds

Number of obs.                     =   812
Number of selected obs.            =   511
Trimming porportion                =   0.0773

------------------------------------------------------------------------------
     guebas2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hotline      |
       lower |   .0442169   .0338336     1.31   0.191    -.0220957    .1105294
       upper |    .127992   .0608102     2.10   0.035     .0088061    .2471778
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     guebas2 |       249    .8192771    .3855634          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     guebas2 |       262    .8740458    .3324328          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
guebas2_ve~e |       248    .8225806    .3827957          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
gueba~de |     248    .8225806    .0243076    .3827957    .7747041    .8704572
guebas~l |     249    .8192771    .0244341    .3855634    .7711523    .8674019
---------+--------------------------------------------------------------------
combined |     497    .8209256    .0172158     .383801    .7871006    .8547505
---------+--------------------------------------------------------------------
    diff |            .0033035    .0344662               -.0644145    .0710216
------------------------------------------------------------------------------
    diff = mean(guebas2_verdade) - mean(guebas2_control)          t =   0.0958
Ho: diff = 0                                     degrees of freedom =      495

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.5382         Pr(|T| > |t|) = 0.9237          Pr(T > t) = 0.4618

Lee (2009) treatment effect bounds

Number of obs.                     =   810
Number of selected obs.            =   497
Trimming porportion                =   0.0301

------------------------------------------------------------------------------
     guebas2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
verdade      |
       lower |  -.0021998   .0364224    -0.06   0.952    -.0735863    .0691868
       upper |   .0288188   .0588087     0.49   0.624    -.0864442    .1440818
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     guebas2 |       249    .8192771    .3855634          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     guebas2 |       248    .8225806    .3827957          0          1


note: results saved to attrition.xml

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
dlakhama2_~l |       249    .0120482    .1093208          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
dlakhama2_~c |       272    .0036765    .0606339          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
dlakha~c |     272    .0036765    .0036765    .0606339   -.0035616    .0109145
dlakha~l |     249    .0120482    .0069279    .1093208   -.0015969    .0256933
---------+--------------------------------------------------------------------
combined |     521    .0076775    .0038277    .0873685    .0001579    .0151972
---------+--------------------------------------------------------------------
    diff |           -.0083717    .0076614               -.0234229    .0066795
------------------------------------------------------------------------------
    diff = mean(dlakhama2_civi~c) - mean(dlakhama2_cont~l)        t =  -1.0927
Ho: diff = 0                                     degrees of freedom =      519

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1375         Pr(|T| > |t|) = 0.2750          Pr(T > t) = 0.8625

Lee (2009) treatment effect bounds

Number of obs.                     =   835
Number of selected obs.            =   521
Trimming porportion                =   0.0601

------------------------------------------------------------------------------
   dlakhama2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
civiceduc    |
       lower |  -.0120482   .0069279    -1.74   0.082    -.0256267    .0015303
       upper |  -.0081366   .0079552    -1.02   0.306    -.0237285    .0074553
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
   dlakhama2 |       249    .0120482    .1093208          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
   dlakhama2 |       272    .0036765    .0606339          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
dlakhama2~ne |       262     .019084    .1370821          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
dlakh~ne |     262     .019084     .008469    .1370821    .0024078    .0357602
dlakha~l |     249    .0120482    .0069279    .1093208   -.0015969    .0256933
---------+--------------------------------------------------------------------
combined |     511    .0156556     .005497    .1242606    .0048561    .0264551
---------+--------------------------------------------------------------------
    diff |            .0070358    .0110039               -.0145828    .0286544
------------------------------------------------------------------------------
    diff = mean(dlakhama2_hotl~e) - mean(dlakhama2_cont~l)        t =   0.6394
Ho: diff = 0                                     degrees of freedom =      509

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.7386         Pr(|T| > |t|) = 0.5229          Pr(T > t) = 0.2614

Lee (2009) treatment effect bounds

Number of obs.                     =   812
Number of selected obs.            =   511
Trimming porportion                =   0.0773

------------------------------------------------------------------------------
   dlakhama2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hotline      |
       lower |  -.0120482   .0069279    -1.74   0.082    -.0256267    .0015303
       upper |   .0086345   .0115396     0.75   0.454    -.0139826    .0312517
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
   dlakhama2 |       249    .0120482    .1093208          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
   dlakhama2 |       262     .019084    .1370821          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
dlakhama2~de |       248    .0201613     .140836          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
dlakh~de |     248    .0201613    .0089431     .140836    .0025468    .0377757
dlakha~l |     249    .0120482    .0069279    .1093208   -.0015969    .0256933
---------+--------------------------------------------------------------------
combined |     497    .0160966    .0056507    .1259738    .0049943    .0271988
---------+--------------------------------------------------------------------
    diff |            .0081131     .011307               -.0141024    .0303286
------------------------------------------------------------------------------
    diff = mean(dlakhama2_verd~e) - mean(dlakhama2_cont~l)        t =   0.7175
Ho: diff = 0                                     degrees of freedom =      495

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.7633         Pr(|T| > |t|) = 0.4734          Pr(T > t) = 0.2367

Lee (2009) treatment effect bounds

Number of obs.                     =   810
Number of selected obs.            =   497
Trimming porportion                =   0.0301

------------------------------------------------------------------------------
   dlakhama2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
verdade      |
       lower |  -.0120482   .0069279    -1.74   0.082    -.0256267    .0015303
       upper |   .0087385   .0115764     0.75   0.450    -.0139509    .0314279
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
   dlakhama2 |       249    .0120482    .1093208          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
   dlakhama2 |       248    .0201613     .140836          0          1


note: results saved to attrition.xml

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
simango2_c~l |       249    .0281124     .165627          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
simango2_c~c |       272    .0367647    .1885305          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
simang~c |     272    .0367647    .0114313    .1885305    .0142592    .0592702
simang~l |     249    .0281124    .0104962     .165627    .0074394    .0487855
---------+--------------------------------------------------------------------
combined |     521    .0326296    .0077911    .1778358    .0173236    .0479355
---------+--------------------------------------------------------------------
    diff |            .0086523    .0156079               -.0220101    .0393146
------------------------------------------------------------------------------
    diff = mean(simango2_civic~c) - mean(simango2_control)        t =   0.5544
Ho: diff = 0                                     degrees of freedom =      519

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.7102         Pr(|T| > |t|) = 0.5796          Pr(T > t) = 0.2898

Lee (2009) treatment effect bounds

Number of obs.                     =   835
Number of selected obs.            =   521
Trimming porportion                =   0.0601

------------------------------------------------------------------------------
    simango2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
civiceduc    |
       lower |  -.0281124   .0104962    -2.68   0.007    -.0486846   -.0075403
       upper |   .0110038   .0161862     0.68   0.497    -.0207205    .0427281
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    simango2 |       249    .0281124     .165627          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    simango2 |       272    .0367647    .1885305          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
simango2_h~e |       262    .0305344    .1723816          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
siman~ne |     262    .0305344    .0106498    .1723816    .0095639    .0515048
simang~l |     249    .0281124    .0104962     .165627    .0074394    .0487855
---------+--------------------------------------------------------------------
combined |     511    .0293542    .0074745    .1689627    .0146697    .0440388
---------+--------------------------------------------------------------------
    diff |            .0024219    .0149681               -.0269849    .0318287
------------------------------------------------------------------------------
    diff = mean(simango2_hotline) - mean(simango2_control)        t =   0.1618
Ho: diff = 0                                     degrees of freedom =      509

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.5642         Pr(|T| > |t|) = 0.8715          Pr(T > t) = 0.4358

Lee (2009) treatment effect bounds

Number of obs.                     =   812
Number of selected obs.            =   511
Trimming porportion                =   0.0773

------------------------------------------------------------------------------
    simango2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hotline      |
       lower |  -.0281124   .0104962    -2.68   0.007    -.0486846   -.0075403
       upper |   .0049799   .0156863     0.32   0.751    -.0257647    .0357245
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    simango2 |       249    .0281124     .165627          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    simango2 |       262    .0305344    .1723816          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
simango2_v~e |       248     .016129    .1262265          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
siman~de |     248     .016129    .0080154    .1262265    .0003418    .0319163
simang~l |     249    .0281124    .0104962     .165627    .0074394    .0487855
---------+--------------------------------------------------------------------
combined |     497    .0221328    .0066057    .1472636    .0091542    .0351114
---------+--------------------------------------------------------------------
    diff |           -.0119834    .0132137               -.0379454    .0139785
------------------------------------------------------------------------------
    diff = mean(simango2_verdade) - mean(simango2_control)        t =  -0.9069
Ho: diff = 0                                     degrees of freedom =      495

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1825         Pr(|T| > |t|) = 0.3649          Pr(T > t) = 0.8175

Lee (2009) treatment effect bounds

Number of obs.                     =   810
Number of selected obs.            =   497
Trimming porportion                =   0.0301

------------------------------------------------------------------------------
    simango2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
verdade      |
       lower |  -.0281124   .0104962    -2.68   0.007    -.0486846   -.0075403
       upper |  -.0114831   .0133809    -0.86   0.391    -.0377092    .0147429
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    simango2 |       249    .0281124     .165627          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    simango2 |       248     .016129    .1262265          0          1


note: results saved to attrition.xml

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
frelimo2_c~l |       252    .8214286    .3837552          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
frelimo2_c~c |       276     .865942    .3413335          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
frelim~c |     276     .865942    .0205459    .3413335    .8254949    .9063892
frelim~l |     252    .8214286    .0241743    .3837552    .7738182    .8690389
---------+--------------------------------------------------------------------
combined |     528     .844697    .0157774    .3625368    .8137027    .8756913
---------+--------------------------------------------------------------------
    diff |            .0445135    .0315578               -.0174814    .1065083
------------------------------------------------------------------------------
    diff = mean(frelimo2_civic~c) - mean(frelimo2_control)        t =   1.4105
Ho: diff = 0                                     degrees of freedom =      526

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9205         Pr(|T| > |t|) = 0.1590          Pr(T > t) = 0.0795

Lee (2009) treatment effect bounds

Number of obs.                     =   842
Number of selected obs.            =   528
Trimming porportion                =   0.0606

------------------------------------------------------------------------------
    frelimo2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
civiceduc    |
       lower |   .0358723   .0334485     1.07   0.284    -.0296855      .10143
       upper |   .1003308   .0589394     1.70   0.089    -.0151882    .2158499
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    frelimo2 |       252    .8214286    .3837552          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    frelimo2 |       276     .865942    .3413335          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
frelimo2_h~e |       269    .8884758    .3153669          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
freli~ne |     269    .8884758    .0192283    .3153669    .8506182    .9263335
frelim~l |     252    .8214286    .0241743    .3837552    .7738182    .8690389
---------+--------------------------------------------------------------------
combined |     521    .8560461    .0153943    .3513804    .8258035    .8862886
---------+--------------------------------------------------------------------
    diff |            .0670473    .0306938                .0067479    .1273466
------------------------------------------------------------------------------
    diff = mean(frelimo2_hotline) - mean(frelimo2_control)        t =   2.1844
Ho: diff = 0                                     degrees of freedom =      519

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9853         Pr(|T| > |t|) = 0.0294          Pr(T > t) = 0.0147

Lee (2009) treatment effect bounds

Number of obs.                     =   822
Number of selected obs.            =   521
Trimming porportion                =   0.0813

------------------------------------------------------------------------------
    frelimo2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hotline      |
       lower |   .0571838   .0325933     1.75   0.079    -.0066979    .1210655
       upper |   .1456261   .0604746     2.41   0.016     .0270981    .2641542
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    frelimo2 |       252    .8214286    .3837552          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    frelimo2 |       269    .8884758    .3153669          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
frelimo2_v~e |       251    .8326693    .3740166          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
freli~de |     251    .8326693    .0236077    .3740166     .786174    .8791647
frelim~l |     252    .8214286    .0241743    .3837552    .7738182    .8690389
---------+--------------------------------------------------------------------
combined |     503    .8270378    .0168805    .3785911    .7938726     .860203
---------+--------------------------------------------------------------------
    diff |            .0112408    .0337911                -.055149    .0776305
------------------------------------------------------------------------------
    diff = mean(frelimo2_verdade) - mean(frelimo2_control)        t =   0.3327
Ho: diff = 0                                     degrees of freedom =      501

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6302         Pr(|T| > |t|) = 0.7395          Pr(T > t) = 0.3698

Lee (2009) treatment effect bounds

Number of obs.                     =   816
Number of selected obs.            =   503
Trimming porportion                =   0.0299

------------------------------------------------------------------------------
    frelimo2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
verdade      |
       lower |    .006086    .035566     0.17   0.864     -.063622     .075794
       upper |   .0368919   .0584842     0.63   0.528    -.0777351    .1515189
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    frelimo2 |       252    .8214286    .3837552          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    frelimo2 |       251    .8326693    .3740166          0          1


note: results saved to attrition.xml

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
renamo2_co~l |       252    .0119048    .1086734          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
renamo2_ci~c |       276    .0108696    .1038774          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
renamo~c |     276    .0108696    .0062527    .1038774   -.0014396    .0231788
renamo~l |     252    .0119048    .0068458    .1086734   -.0015777    .0253873
---------+--------------------------------------------------------------------
combined |     528    .0113636    .0046171    .1060935    .0022934    .0204339
---------+--------------------------------------------------------------------
    diff |           -.0010352    .0092525               -.0192115    .0171411
------------------------------------------------------------------------------
    diff = mean(renamo2_civice~c) - mean(renamo2_control)         t =  -0.1119
Ho: diff = 0                                     degrees of freedom =      526

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.4555         Pr(|T| > |t|) = 0.9110          Pr(T > t) = 0.5445

Lee (2009) treatment effect bounds

Number of obs.                     =   842
Number of selected obs.            =   528
Trimming porportion                =   0.0606

------------------------------------------------------------------------------
     renamo2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
civiceduc    |
       lower |  -.0119048   .0068458    -1.74   0.082    -.0253222    .0015127
       upper |  -.0003346   .0095594    -0.03   0.972    -.0190707    .0184016
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     renamo2 |       252    .0119048    .1086734          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     renamo2 |       276    .0108696    .1038774          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
renamo2_ho~e |       269    .0260223    .1594983          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
renam~ne |     269    .0260223    .0097248    .1594983    .0068756     .045169
renamo~l |     252    .0119048    .0068458    .1086734   -.0015777    .0253873
---------+--------------------------------------------------------------------
combined |     521    .0191939    .0060169    .1373377    .0073735    .0310142
---------+--------------------------------------------------------------------
    diff |            .0141175    .0120358               -.0095274    .0377624
------------------------------------------------------------------------------
    diff = mean(renamo2_hotline) - mean(renamo2_control)          t =   1.1730
Ho: diff = 0                                     degrees of freedom =      519

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.8793         Pr(|T| > |t|) = 0.2413          Pr(T > t) = 0.1207

Lee (2009) treatment effect bounds

Number of obs.                     =   822
Number of selected obs.            =   521
Trimming porportion                =   0.0813

------------------------------------------------------------------------------
     renamo2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
hotline      |
       lower |  -.0119048   .0068458    -1.74   0.082    -.0253222    .0015127
       upper |    .016419   .0126787     1.30   0.195    -.0084308    .0412688
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     renamo2 |       252    .0119048    .1086734          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     renamo2 |       269    .0260223    .1594983          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
renamo2_ve~e |       251    .0159363    .1254792          0          1

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
renam~de |     251    .0159363    .0079202    .1254792    .0003375     .031535
renamo~l |     252    .0119048    .0068458    .1086734   -.0015777    .0253873
---------+--------------------------------------------------------------------
combined |     503    .0139165    .0052284    .1172611    .0036442    .0241888
---------+--------------------------------------------------------------------
    diff |            .0040315    .0104657               -.0165306    .0245936
------------------------------------------------------------------------------
    diff = mean(renamo2_verdade) - mean(renamo2_control)          t =   0.3852
Ho: diff = 0                                     degrees of freedom =      501

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6499         Pr(|T| > |t|) = 0.7002          Pr(T > t) = 0.3501

Lee (2009) treatment effect bounds

Number of obs.                     =   816
Number of selected obs.            =   503
Trimming porportion                =   0.0299

------------------------------------------------------------------------------
     renamo2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
verdade      |
       lower |  -.0119048   .0068458    -1.74   0.082    -.0253222    .0015127
       upper |   .0045224   .0106806     0.42   0.672    -.0164112     .025456
------------------------------------------------------------------------------

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     renamo2 |       252    .0119048    .1086734          0          1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     renamo2 |       251    .0159363    .1254792          0          1


note: results saved to attrition.xml

. 
. global list2=""

. 
. matrix define means=(m_tresp_1, m_tfinger_1, m_tseen_1, m_intt_1, m_carta_1, m_guebas2_1, m_dl
> akhama2_1, m_simango2_1, m_frelimo2_1, m_renamo2_1 \ se_tresp_1, se_tfinger_1, se_tseen_1, se_
> intt_1, se_carta_1, se_guebas2_1, se_dlakhama2_1, se_simango2_1, se_frelimo2_1, se_renamo2_1 \
>  p_tresp_1, p_tfinger_1, p_tseen_1, p_intt_1, p_carta_1, p_guebas2_1, p_dlakhama2_1, p_simango
> 2_1, p_frelimo2_1, p_renamo2_1 \ m_tresp_2, m_tfinger_2, m_tseen_2, m_intt_2, m_carta_2, m_gue
> bas2_2, m_dlakhama2_2, m_simango2_2, m_frelimo2_2, m_renamo2_2 \ se_tresp_2, se_tfinger_2, se_
> tseen_2, se_intt_2, se_carta_2, se_guebas2_2, se_dlakhama2_2, se_simango2_2, se_frelimo2_2, se
> _renamo2_2 \ p_tresp_2, p_tfinger_2, p_tseen_2, p_intt_2, p_carta_2, p_guebas2_2, p_dlakhama2_
> 2, p_simango2_2, p_frelimo2_2, p_renamo2_2 \ m_tresp_3, m_tfinger_3, m_tseen_3, m_intt_3, m_ca
> rta_3, m_guebas2_3, m_dlakhama2_3, m_simango2_3, m_frelimo2_3, m_renamo2_3 \ se_tresp_3, se_tf
> inger_3, se_tseen_3, se_intt_3, se_carta_3, se_guebas2_3, se_dlakhama2_3, se_simango2_3, se_fr
> elimo2_3, se_renamo2_3 \ p_tresp_3, p_tfinger_3, p_tseen_3, p_intt_3, p_carta_3, p_guebas2_3, 
> p_dlakhama2_3, p_simango2_3, p_frelimo2_3, p_renamo2_3 \ n_tresp_1_1, n_tfinger_1_1, n_tseen_1
> _1, n_intt_1_1, n_carta_1_1, n_guebas2_1_1, n_dlakhama2_1_1, n_simango2_1_1, n_frelimo2_1_1, n
> _renamo2_1_1 \ n_tresp_1_2, n_tfinger_1_2, n_tseen_1_2, n_intt_1_2, n_carta_1_2, n_guebas2_1_2
> , n_dlakhama2_1_2, n_simango2_1_2, n_frelimo2_1_2, n_renamo2_1_2 \ n_tresp_2_1, n_tfinger_2_1,
>  n_tseen_2_1, n_intt_2_1, n_carta_2_1, n_guebas2_2_1, n_dlakhama2_2_1, n_simango2_2_1, n_freli
> mo2_2_1, n_renamo2_2_1 \ n_tresp_2_2, n_tfinger_2_2, n_tseen_2_2, n_intt_2_2, n_carta_2_2, n_g
> uebas2_2_2, n_dlakhama2_2_2, n_simango2_2_2, n_frelimo2_1_1, n_renamo2_2_2 \ n_tresp_3_1, n_tf
> inger_3_1, n_tseen_3_1, n_intt_3_1, n_carta_3_1, n_guebas2_3_1, n_dlakhama2_3_1, n_simango2_3_
> 1, n_frelimo2_3_1, n_renamo2_3_1 \ n_tresp_3_2, n_tfinger_3_2, n_tseen_3_2, n_intt_3_2, n_cart
> a_3_2, n_guebas2_3_2, n_dlakhama2_3_2, n_simango2_3_2, n_frelimo2_3_2, n_renamo2_3_2)

. global list2="$list2" + " means"

. 
. xml_tab $list2, save("attrition.xml") append sheet("lee stats") 


note: results saved to attrition.xml

. estimates clear

. 
. log close
      name:  <unnamed>
       log:  codevoteduc.log
  log type:  text
 closed on:  19 Apr 2016, 18:14:02
------------------------------------------------------------------------------------------------
